Gas remote pressure control method and system

Abstract

The invention relates to the technical field of energy control, in particular to a gas remote pressure control method and system, and the method comprises the steps: collecting pressure data in a gas pipeline in real time, and obtaining real-time pressure data; based on a self-adaptive control algorithm, carrying out change trend analysis on the pressure data, and dynamically adjusting a control instruction; monitoring the adjusted pressure data, comparing the adjusted pressure data with a target pressure value, secondarily adjusting the control parameters according to a comparison result, and obtaining a secondarily optimized control instruction; analyzing the historical control data based on iterative optimization and a self-learning algorithm, and generating a historical data driving optimization instruction; and the historical data driving optimization instruction and the control instruction after secondary optimization are coordinated, and a final pressure control instruction is obtained through compatibility check and priority adjustment. According to the invention, the problem of slow real-time response in traditional gas pressure control is effectively solved, and the response speed and the control precision of the system are improved.

Description

Technical Field

[0001] The present invention relates to the field of energy control technology, and in particular to a gas remote pressure control method and system. Background Art

[0002] In modern gas pipeline systems, pressure control is a key link to ensure safe and stable operation. However, traditional gas pressure control methods often rely on fixed control strategies and manual monitoring, which are difficult to adapt to the dynamic changes of actual working conditions. This static control mode cannot respond to pressure fluctuations in the gas pipeline in a timely manner, which can easily lead to control errors and affect the safety and efficiency of the system. In addition, the data collection and processing of traditional methods are delayed and lack real-time performance. There is also a lack of intelligent optimization methods in the control strategy, and it is impossible to make full use of historical data for adjustment and optimization.

[0003] The information disclosed in this background technology section is only intended to deepen the understanding of the overall background technology of the present disclosure, and should not be regarded as acknowledging or suggesting in any form that the information constitutes the prior art known to those skilled in the art. Summary of the invention

[0004] The present invention provides a gas remote pressure control method and system, thereby effectively solving the problems pointed out in the background technology.

[0005] In order to achieve the above object, the technical solution adopted by the present invention is: A gas remote pressure control method, comprising: Collecting pressure data in the gas pipeline in real time, and preprocessing the pressure data to obtain real-time pressure data; Based on the adaptive control algorithm, the pressure data is analyzed for change trends, and the control instructions are dynamically adjusted according to the analysis results; Monitoring the adjusted pressure data, and comparing the adjusted pressure data with the target pressure value, adjusting the control parameters for a second time according to the comparison result, and obtaining a control instruction after secondary optimization; Analyze historical control data based on iterative optimization and self-learning algorithms to generate historical data-driven optimization instructions; The historical data driven optimization instructions and the secondary optimized control instructions are coordinated, and the final pressure control instructions are obtained through compatibility checking and priority adjustment.

[0006] Furthermore, the compatibility check includes: Conduct preliminary screening of historical data-driven optimization instructions and secondary optimized control instructions to identify differences in objectives, scope, and execution conditions between the two. Analyze the specific contents of the historical data driven optimization instructions and the secondary optimized control instructions to detect potential conflicts; The dependency relationship between the historical data driven optimization instructions and the secondary optimized control instructions is checked, and the execution order of the dependent instructions is adjusted.

[0007] Furthermore, the priority adjustment includes: According to the urgency of the control command and the system security requirements, the priority of each control command is assigned to obtain the security response priority; Analyze the effects of the historical data-driven optimization instructions and the secondary optimized control instructions in historical operation to obtain historical operation effects; According to the security response priority and historical operation results, an adjusted priority order is obtained.

[0008] Furthermore, according to the compatibility check and priority adjustment results, the final pressure control instruction is obtained, including: Integrate the results of compatibility check and priority adjustment to determine the execution order and specific parameter settings of each control instruction, and identify the conflict points that could not be eliminated in the compatibility check; For conflicts identified in the compatibility check, select the higher priority instruction as the main control instruction according to the result of priority adjustment to handle the conflict point; The instructions after conflict resolution are merged, and the historical data driven optimization instructions and the control instructions after secondary optimization are integrated into a unified instruction set to generate the final pressure control instructions.

[0009] Furthermore, based on the adaptive control algorithm, the pressure data is analyzed for change trends, and the control instructions are dynamically adjusted according to the analysis results, including: Smoothing the real-time pressure data to obtain a pressure data sequence reflecting an actual trend; By using an adaptive control algorithm, trend detection is performed on the pressure data sequence reflecting the actual trend to identify the rising, falling and stable trends of the pressure; Calculate the pressure change rate, and compare the pressure change rate with the threshold to determine whether the control instruction needs to be adjusted. If it exceeds the set threshold, trigger the instruction adjustment process; The parameters of the instruction are dynamically adjusted according to the trend detection result and the pressure change rate.

[0010] Further, the adjusted pressure data is monitored, and the adjusted pressure data is compared with the target pressure value, and the control parameters are adjusted for a second time according to the comparison result, and the control instructions after the second optimization are obtained, including: Continuously monitor the adjusted pressure data and record the real-time pressure value after the initial adjustment; Comparing the monitored real-time pressure value with a predetermined target pressure value, calculating the difference and determining the deviation of the current pressure state; According to the deviation of the pressure state, the size and direction of the difference are judged to obtain the deviation analysis result, and according to the deviation analysis result, the control instruction after secondary optimization is obtained.

[0011] Furthermore, historical control data is analyzed based on iterative optimization and self-learning algorithms to generate historical data driven optimization instructions, including: Preprocessing the historical control data; extracting features related to pressure control from the historical control data based on an iterative optimization algorithm; The extracted features are used to train the optimization model based on a self-learning algorithm, and through an iterative optimization process, the historical data-driven optimization instructions are generated according to the trained model.

[0012] Furthermore, the optimization model is a deep neural network model.

[0013] A gas remote pressure control system, the system comprising: A pressure data acquisition module collects pressure data in the gas pipeline in real time and pre-processes the pressure data to obtain real-time pressure data; An adaptive instruction adjustment module, based on an adaptive control algorithm, performs a change trend analysis on the pressure data and dynamically adjusts the control instruction according to the analysis result; A secondary instruction optimization module monitors the adjusted pressure data, compares the adjusted pressure data with the target pressure value, adjusts the control parameters secondary according to the comparison result, and obtains the secondary optimized control instruction; The historical instruction generation module analyzes historical control data based on iterative optimization and self-learning algorithms to generate historical data-driven optimization instructions; The final instruction acquisition module coordinates the historical data driven optimization instructions and the secondary optimized control instructions, and obtains the final pressure control instructions through compatibility check and priority adjustment.

[0014] Furthermore, the adaptive instruction adjustment module includes: A data sequence acquisition unit performs smoothing processing on the real-time pressure data to obtain a pressure data sequence reflecting an actual trend; A pressure trend detection unit, which performs trend detection on the pressure data sequence reflecting the actual trend through an adaptive control algorithm, and identifies the rising, falling and stable trends of the pressure; A pressure change calculation unit calculates the pressure change rate and compares the pressure change rate with the threshold to determine whether the control instruction needs to be adjusted. If the pressure exceeds the set threshold, the instruction adjustment process is triggered; The instruction dynamic adjustment unit dynamically adjusts the parameters of the instruction according to the trend detection result and the pressure change rate.

[0015] The technical solution of the present invention can achieve the following technical effects: It effectively solves the problem of slow real-time response in traditional gas pressure control and improves the system's response speed and control accuracy. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0017] Figure 1 It is a flow chart of a gas remote pressure control method; Figure 2 A flowchart of compatibility check is shown below; Figure 3 A flowchart for priority adjustment; Figure 4 A flow chart for obtaining the final pressure control instruction; Figure 5 A schematic diagram of a flow chart for dynamically adjusting control instructions according to analysis results; Figure 6 A flow chart of the control instructions after secondary optimization is obtained; Figure 7 Schematic diagram of the process for generating historical data driven optimization instructions. DETAILED DESCRIPTION

[0018] The technical solutions in the embodiments of the present invention will be described clearly and completely below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments.

[0019] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art of the present invention. The terms used in the specification of the present invention are only for the purpose of describing specific embodiments and are not intended to limit the present invention. The term "and / or" used herein includes any and all combinations of one or more related listed items.

[0020] Embodiment 1 like Figure 1 As shown, the present invention provides a method for remote gas pressure control, the method comprising: S1: real-time collection of pressure data in the gas pipeline, and pre-processing of the pressure data to obtain real-time pressure data; Specifically, firstly, the pressure data in the pipeline is collected in real time through the high-precision pressure sensor arranged in the gas pipeline. During the collection process, the sampling frequency of the data is set according to the requirements of the system to ensure the continuity and integrity of the data; then, the collected raw pressure data is cleaned to remove noise and obviously abnormal readings in the data. Common cleaning methods include mean filtering or median filtering to improve the reliability of the data; then, the cleaned pressure data is smoothed, and smoothing algorithms such as the sliding average method are used to reduce the impact of short-term pressure fluctuations on system control, thereby obtaining more stable pressure change trend data; finally, the smoothed pressure data is formatted to meet the input requirements of subsequent analysis and control, such as converting the data into a standardized time series format, ready to be input into the adaptive control algorithm for further processing and analysis.

[0021] S2: Based on the adaptive control algorithm, the pressure data is analyzed for change trends, and the control instructions are dynamically adjusted according to the analysis results; Specifically, the changing trend of pressure data is analyzed through adaptive control algorithms to understand the dynamic changes of pressure in the gas pipeline in real time. According to the analysis results, the control instructions can be adjusted in a timely and dynamic manner to ensure that the pressure in the gas pipeline is always maintained within the set safety range, thereby avoiding potential risks caused by excessive pressure fluctuations or exceeding the safety range. Through this dynamic adjustment, it is possible to respond more flexibly to various operating conditions and improve the operating efficiency and safety of the gas pipeline.

[0022] S3: monitor the adjusted pressure data, compare the adjusted pressure data with the target pressure value, adjust the control parameters for the second time according to the comparison result, and obtain the control instructions after the second optimization; Specifically, after the initial adjustment, the pressure control effect of the system is verified and further optimized. By monitoring the adjusted pressure data and comparing it with the target pressure value, it can be determined whether the effect of the current control instruction meets expectations. If there is a deviation or deficiency, a secondary adjustment is made based on the comparison result to further optimize the control parameters. This process aims to refine the control and ensure that the system pressure is more accurately maintained near the target value, thereby improving control accuracy and stability, and ultimately ensuring the safe and efficient operation of the gas pipeline.

[0023] S4: Analyze historical control data based on iterative optimization and self-learning algorithms to generate historical data-driven optimization strategies; Specifically, iterative optimization and self-learning algorithms are used to conduct in-depth analysis of historical control data to discover the patterns and trends under different operating conditions, thereby generating more intelligent and adaptable optimization strategies. Through this process, we can learn and accumulate past experiences and lessons, automatically adjust and optimize future control strategies, and enable them to better cope with complex and changeable operating conditions.

[0024] S5: Coordinate the historical data-driven optimization strategy and the secondary optimized control instructions, and obtain the final pressure control instructions through compatibility check and priority adjustment.

[0025] Specifically, the historical data-driven optimization strategy is effectively integrated with the secondary optimized control instructions to ensure that they can work together in actual applications. Through compatibility checks, possible conflicts can be identified and resolved to ensure the logical consistency between different strategies and instructions. Priority adjustment is used to determine which instructions should be executed first when multiple control instructions coexist, thereby optimizing the overall control effect of the system. Finally, through this coordination process, a comprehensive and optimal pressure control instruction is generated to ensure that the system can operate safely and efficiently under various working conditions.

[0026] The present invention effectively solves the problem of slow real-time response in traditional gas pressure control, and improves the response speed and control accuracy of the system.

[0027] As a preferred embodiment of the above, Figure 2 As shown, the compatibility check includes: A10: Perform a preliminary screening of historical data-driven optimization instructions and secondary optimized control instructions to identify differences in objectives, scope, and execution conditions between the two. A20: Analyze the specific contents of historical data-driven optimization instructions and secondary optimized control instructions to detect potential conflicts; A30: Check the dependency between historical data-driven optimization instructions and secondary optimized control instructions, and adjust the execution order of the dependent instructions.

[0028] Specifically, first, the historical data-driven optimization instructions and the control instructions after secondary optimization are preliminarily screened, focusing on identifying the differences between the two in terms of control objectives, scope of application, and execution conditions. Through screening, it is clear which instructions may have conflicting or inconsistent execution logic, laying the foundation for subsequent detailed analysis; Next, the specific contents of the historical data-driven optimization instructions and the control instructions after secondary optimization after preliminary screening are deeply analyzed to detect potential conflicts. For example, check whether the instructions require the system to perform contradictory operations at the same time, such as one instruction requires increasing pressure while the other instruction requires reducing pressure at the same time. Through conflict detection, identify contradictory instructions that may cause abnormal system operation; Finally, check the dependency relationship between the historical data-driven optimization instructions and the control instructions after secondary optimization to ensure the rationality of the execution order. For instructions with dependencies, make appropriate execution order adjustments to ensure that the upstream instructions in the dependency chain can be executed first to prevent system failure or failure to achieve the expected control effect due to sequence errors.

[0029] As a preferred embodiment of the above, Figure 3 As shown, priority adjustments include: B10: According to the urgency of the control command and the system security requirements, the priority of each control command is assigned to obtain the security response priority; B20: Analyze the effects of historical data-driven optimization instructions and secondary optimized control instructions in historical operation to obtain historical operation results; B30: Obtain the adjusted priority order based on security response priorities and historical operating results.

[0030] Specifically, first, the initial priority of each control instruction is assigned according to the urgency of each control instruction and the system safety requirements. Priority is given to instructions involving system safety, such as emergency shutdown or rapid response measures, and they are given higher priority to form a safety response priority; next, the effects of historical data-driven optimization instructions and secondary optimized control instructions in past operations are analyzed, and the effectiveness and reliability of these instructions in actual applications are determined through the evaluation of historical operation data, and which instructions perform best under different working conditions are identified, and their priorities are adjusted accordingly; finally, combined with the safety response priority and historical operation results, the safety and actual effects of the control instructions are comprehensively considered, and the execution order of each control instruction is rearranged. The adjusted priority order will ensure efficient and safe system operation, and give priority to those instructions that can both ensure system safety and perform well in history.

[0031] As a preferred embodiment of the above, Figure 4 As shown, according to the compatibility check and priority adjustment results, the final pressure control instructions are obtained, including: C10: Integrate the results of compatibility check and priority adjustment, determine the execution order and specific parameter settings of each control instruction, and identify the conflict points that cannot be eliminated in the compatibility check; C20: For conflicts identified in the compatibility check, select the higher priority instruction as the main control instruction according to the result of priority adjustment and handle the conflict point; C30: Fusion the conflict-resolved instructions, integrating the historical data-driven optimization instructions and the secondary optimized control instructions into a unified instruction set to generate the final pressure control instructions.

[0032] Specifically, first, the results of the compatibility check and priority adjustment are integrated to clarify the execution order and specific parameter settings of each control instruction. In this process, the conflict points that cannot be completely eliminated in the compatibility check are identified to provide a basis for subsequent conflict handling. For the conflict points identified in the compatibility check, based on the results of the priority adjustment, the instructions with higher priority are selected as the main control instructions. By adjusting or ignoring part of the content of the low-priority instructions, the conflicts are resolved to ensure that the main control instructions can be executed smoothly, thereby ensuring the safety and efficiency of the system. After the conflict handling is completed, the processed control instructions are fused, and the historical data-driven optimization instructions and the secondary optimized control instructions are integrated into a unified instruction set. In this fusion process, the coordination and consistency between the instructions are ensured to generate the final pressure control instructions.

[0033] As a preferred embodiment of the above, Figure 5 As shown, step S2, based on the adaptive control algorithm, analyzes the change trend of the pressure data, and dynamically adjusts the control instructions according to the analysis results, including: S21: Smoothing the real-time pressure data to obtain a pressure data sequence reflecting the actual trend; S22: Through the adaptive control algorithm, trend detection is performed on the pressure data sequence reflecting the actual trend to identify the rising, falling and stable trends of the pressure; S23: Calculate the pressure change rate and compare the pressure change rate with a threshold to determine whether the control command needs to be adjusted. If the pressure exceeds the set threshold, the command adjustment process is triggered; S24: Dynamically adjust the parameters of the command according to the trend detection result and the pressure change rate.

[0034] Specifically, first, the real-time pressure data is smoothed to remove short-term fluctuations and noise to obtain a pressure data sequence that can more accurately reflect the actual pressure change trend. Commonly used smoothing methods include sliding average method or exponential smoothing method; next, based on the smoothed data, the derivative or difference of the pressure data is calculated to detect the direction of data change in real time, and by analyzing the pressure change pattern over a period of time, an upward trend, a downward trend or a stable trend is identified. For example, an increase in pressure at multiple consecutive time points indicates an upward trend, and vice versa, a downward trend. When the change is very small or stable, it is determined to be a stable trend. According to the identified trend, it is classified into three types: rising, falling or stable; the rate of change of the pressure data is calculated to quantify the speed and amplitude of the pressure change. Then, the calculated pressure change rate is compared with the preset threshold to determine whether the current pressure change exceeds the set allowable range. If the pressure change rate exceeds the set threshold, the control instruction adjustment process is triggered to cope with the larger pressure change; based on the trend detection results and the pressure change rate judgment, the control instruction parameters are dynamically adjusted, including adjusting the control target value, response speed, and adjustment range, to ensure that the system can respond to pressure changes quickly and accurately and keep the pressure within the set safety range.

[0035] As a preferred embodiment of the above, Figure 6 As shown, step S3 monitors the adjusted pressure data, compares the adjusted pressure data with the target pressure value, adjusts the control parameters for a second time according to the comparison result, and obtains the control instructions after the second optimization, including: S31: continuously monitoring the adjusted pressure data, and recording the real-time pressure value after the initial adjustment; S32: comparing the monitored real-time pressure value with the predetermined target pressure value, calculating the difference and determining the deviation of the current pressure state; S33: According to the deviation of the pressure state, the size and direction of the difference are determined to obtain the deviation analysis result, and according to the deviation analysis result, the control instruction after secondary optimization is obtained.

[0036] Specifically, after the initial adjustment of the control instructions, the pressure data in the gas pipeline is monitored in real time. The monitoring at this stage is not only a verification of the adjustment effect, but also provides basic data for the subsequent secondary optimization. In this process, the real-time pressure values ​​are continuously recorded through high-frequency sampling. These data will reflect the pressure changes after the adjustment; the monitored real-time pressure values ​​will be compared with the predetermined target pressure values ​​to calculate the difference between the two, and analyze whether these differences are gradually decreasing, gradually increasing, or fluctuating within a certain range, so as to judge the effectiveness of the current control strategy; after obtaining the pressure difference, it is also necessary to judge factors such as the direction and duration of the difference. For example, if the difference continues to increase over a period of time, it means that the system may be gradually deviating from the target state and corrective measures need to be taken quickly. Based on these analysis results, the system will generate secondary optimized control instructions, which will usually adjust the control parameters more accurately to eliminate or reduce the difference.

[0037] As a preferred embodiment of the above, Figure 7 As shown, step S4, analyzing the historical control data based on iterative optimization and self-learning algorithms to generate historical data driven optimization instructions, includes: S41: preprocessing historical control data; S42: Extracting features related to pressure control from historical control data based on an iterative optimization algorithm; S43: Using the extracted features to train the optimization model based on the self-learning algorithm, and through an iterative optimization process, generating historical data driven optimization instructions according to the trained model.

[0038] Specifically, first, the collected historical control data is preprocessed. This process includes data cleaning, denoising and outlier processing to ensure the accuracy and consistency of the data. The preprocessing may also include data standardization so that the subsequent algorithms can extract features and train more effectively. After completing the data preprocessing, the iterative optimization algorithm is used to extract key features related to pressure control from the historical control data. These features may include pressure change patterns, response effects of control instructions, system stability indicators, etc. The iterative optimization algorithm can effectively identify the features that best reflect the system behavior through repeated adjustment and optimization, thereby providing high-quality input data for the subsequent self-learning algorithm. Next, based on the self-learning algorithm, the extracted key features are used to train the optimization model. The self-learning algorithm can adapt to the complex dynamic changes of the system through continuous learning and adjustment. During the training process, the model will continuously correct and improve its own prediction capabilities through multiple iterative optimizations. Finally, based on the trained model, historical data-driven optimization instructions are generated. These instructions are based on a deep understanding of past system performance and can more effectively cope with future changes in operating conditions.

[0039] As a preferred embodiment of the above embodiment, the optimization model is a deep neural network model.

[0040] Specifically, the pressure control of gas pipelines involves complex nonlinear dynamic changes and multi-dimensional data characteristics. The deep neural network model has powerful nonlinear mapping capabilities and can deeply explore and learn complex patterns and relationships in historical control data, which makes it particularly suitable for handling the variable operating conditions and complex control requirements involved in this solution. First, according to the system requirements and the characteristics of historical data, the structure of the deep neural network model is designed, which usually includes an input layer, multiple hidden layers and an output layer. The input layer receives the extracted key features, the hidden layer performs deep learning through the nonlinear combination of multiple neurons, and the output layer generates optimized control instructions or prediction results. After the model structure is determined, the parameters of the model are initialized, which includes the setting of neuron weights and biases. Usually, random initialization or He initialization is used to ensure that the model can learn effectively in the early stage of training. The features extracted from the historical control data are used as input to train the deep neural network model. During the training process, the self-learning algorithm is used to gradually adjust the weights and biases of the model to minimize the loss function. Commonly used training methods include back propagation algorithm and gradient descent method, which continuously optimize the prediction accuracy of the model through multiple rounds of iterations. During the model training process, the model is evaluated through cross-validation or validation set to detect its performance on unseen data. According to the validation results, the model structure or training parameters are adjusted to improve the generalization ability of the model and prevent overfitting or underfitting. After the training is completed, the trained deep neural network model is used to predict new data to generate optimized control instructions driven by historical data.

[0041] Embodiment 2 Based on the same inventive concept as the gas remote pressure control method in the aforementioned embodiment, the present invention also provides a gas remote pressure control system, the system comprising: The pressure data acquisition module collects the pressure data in the gas pipeline in real time and pre-processes the pressure data to obtain real-time pressure data; The adaptive command adjustment module analyzes the change trend of pressure data based on the adaptive control algorithm and dynamically adjusts the control command according to the analysis results; The secondary instruction optimization module monitors the adjusted pressure data and compares the adjusted pressure data with the target pressure value, adjusts the control parameters secondary according to the comparison result, and obtains the secondary optimized control instruction; The historical instruction generation module analyzes historical control data based on iterative optimization and self-learning algorithms to generate historical data-driven optimization instructions; The final instruction acquisition module coordinates the historical data-driven optimization instructions and the secondary optimized control instructions, and obtains the final pressure control instructions through compatibility checks and priority adjustments.

[0042] The above-mentioned control system in the present invention can effectively implement the gas remote pressure control method, and the technical effects that can be achieved are as described in the above-mentioned embodiments, which will not be repeated here.

[0043] As a preferred embodiment of the above, the adaptive instruction adjustment module includes: The data sequence acquisition unit performs smoothing processing on the real-time pressure data to obtain a pressure data sequence reflecting the actual trend; The pressure trend detection unit performs trend detection on the pressure data sequence reflecting the actual trend through an adaptive control algorithm, and identifies the rising, falling and stable trends of the pressure; A pressure change calculation unit calculates the pressure change rate and compares the pressure change rate with a threshold value to determine whether the control instruction needs to be adjusted. If the pressure exceeds the set threshold value, the instruction adjustment process is triggered; The command dynamic adjustment unit dynamically adjusts the command parameters according to the trend detection result and the pressure change rate.

[0044] Similarly, the above-mentioned optimization schemes for the system can also respectively achieve the optimization effects corresponding to the method in Example 1, which will not be repeated here.

[0045] Although the present application has been described in conjunction with specific features and embodiments thereof, it is obvious that various modifications and combinations may be made thereto without departing from the spirit and scope of the present application. Accordingly, this specification and the accompanying drawings are merely exemplary illustrations of the present application as defined therein, and are deemed to have covered any and all modifications, variations, combinations or equivalents within the scope of the present application. Obviously, a person skilled in the art may make various modifications and variations to the present application without departing from the scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the present application and its equivalents, the present application is intended to include these modifications and variations.

Claims

1. A method for remote gas pressure control, characterized in that: include: Collecting pressure data in the gas pipeline in real time, and preprocessing the pressure data to obtain real-time pressure data; Based on the adaptive control algorithm, the pressure data is analyzed for change trends, and the control instructions are dynamically adjusted according to the analysis results; Monitoring the adjusted pressure data, and comparing the adjusted pressure data with the target pressure value, adjusting the control parameters for a second time according to the comparison result, and obtaining a control instruction after secondary optimization; Analyze historical control data based on iterative optimization and self-learning algorithms to generate historical data-driven optimization instructions; The historical data driven optimization instructions and the secondary optimized control instructions are coordinated, and the final pressure control instructions are obtained through compatibility checking and priority adjustment.

2. The method for controlling the remote gas pressure according to claim 1, characterized in that: The compatibility check includes: Conduct preliminary screening of historical data-driven optimization instructions and secondary optimized control instructions to identify differences in objectives, scope, and execution conditions between the two. Analyze the specific contents of the historical data driven optimization instructions and the secondary optimized control instructions to detect potential conflicts; The dependency relationship between the historical data driven optimization instructions and the secondary optimized control instructions is checked, and the execution order of the dependent instructions is adjusted.

3. The method for controlling the remote gas pressure according to claim 1, characterized in that: The priority adjustment includes: According to the urgency of the control command and the system security requirements, the priority of each control command is assigned to obtain the security response priority; Analyze the effects of the historical data-driven optimization instructions and the secondary optimized control instructions in historical operation to obtain historical operation effects; According to the security response priority and historical operation results, an adjusted priority order is obtained.

4. The method for controlling the gas remote pressure according to claim 1, characterized in that: According to the compatibility check and priority adjustment results, the final pressure control instructions are obtained, including: Integrate the results of compatibility check and priority adjustment to determine the execution order and specific parameter settings of each control instruction, and identify the conflict points that could not be eliminated in the compatibility check; For conflicts identified in the compatibility check, select the higher priority instruction as the main control instruction according to the result of priority adjustment to handle the conflict point; The instructions after conflict resolution are merged, and the historical data driven optimization instructions and the control instructions after secondary optimization are integrated into a unified instruction set to generate the final pressure control instructions.

5. The method for controlling the remote gas pressure according to claim 1, characterized in that: Based on the adaptive control algorithm, the pressure data is analyzed for change trends, and the control instructions are dynamically adjusted according to the analysis results, including: Smoothing the real-time pressure data to obtain a pressure data sequence reflecting an actual trend; By using an adaptive control algorithm, trend detection is performed on the pressure data sequence reflecting the actual trend to identify the rising, falling and stable trends of the pressure; Calculate the pressure change rate, and compare the pressure change rate with the threshold to determine whether the control instruction needs to be adjusted. If it exceeds the set threshold, trigger the instruction adjustment process; The parameters of the instruction are dynamically adjusted according to the trend detection result and the pressure change rate.

6. The method for controlling the remote gas pressure according to claim 1, characterized in that: The adjusted pressure data is monitored, and the adjusted pressure data is compared with the target pressure value, and the control parameters are adjusted for a second time according to the comparison result, and the control instructions after the second optimization are obtained, including: Continuously monitor the adjusted pressure data and record the real-time pressure value after the initial adjustment; Comparing the monitored real-time pressure value with a predetermined target pressure value, calculating the difference and determining the deviation of the current pressure state; According to the deviation of the pressure state, the size and direction of the difference are judged to obtain the deviation analysis result, and according to the deviation analysis result, the control instruction after secondary optimization is obtained.

7. The method for controlling the remote gas pressure according to claim 1, characterized in that: Analyze historical control data based on iterative optimization and self-learning algorithms to generate historical data-driven optimization instructions, including: Preprocessing the historical control data; extracting features related to pressure control from the historical control data based on an iterative optimization algorithm; The extracted features are used to train the optimization model based on a self-learning algorithm, and through an iterative optimization process, the historical data-driven optimization instructions are generated according to the trained model.

8. The method for controlling the remote gas pressure according to claim 7, characterized in that: The optimization model is a deep neural network model.

9. A gas remote pressure control system, characterized in that: The system comprises: A pressure data acquisition module collects pressure data in the gas pipeline in real time and pre-processes the pressure data to obtain real-time pressure data; An adaptive instruction adjustment module, based on an adaptive control algorithm, performs a change trend analysis on the pressure data and dynamically adjusts the control instruction according to the analysis result; A secondary instruction optimization module monitors the adjusted pressure data, compares the adjusted pressure data with the target pressure value, adjusts the control parameters secondary according to the comparison result, and obtains the secondary optimized control instruction; The historical instruction generation module analyzes historical control data based on iterative optimization and self-learning algorithms to generate historical data-driven optimization instructions; The final instruction acquisition module coordinates the historical data driven optimization instructions and the secondary optimized control instructions, and obtains the final pressure control instructions through compatibility check and priority adjustment.

10. The gas remote pressure control system according to claim 9, characterized in that: The adaptive instruction adjustment module comprises: A data sequence acquisition unit performs smoothing processing on the real-time pressure data to obtain a pressure data sequence reflecting an actual trend; A pressure trend detection unit, which performs trend detection on the pressure data sequence reflecting the actual trend through an adaptive control algorithm, and identifies the rising, falling and stable trends of the pressure; A pressure change calculation unit calculates the pressure change rate and compares the pressure change rate with the threshold to determine whether the control instruction needs to be adjusted. If the pressure exceeds the set threshold, the instruction adjustment process is triggered; The instruction dynamic adjustment unit dynamically adjusts the parameters of the instruction according to the trend detection result and the pressure change rate.

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