Gas pipeline self-adaptive pressure compensation control system based on internet of things monitoring technology

Abstract

The present application belongs to the technical field of gas pipeline regulation and control, and specifically relates to a gas pipeline self-adaptive pressure compensation control system based on Internet of Things monitoring technology, which comprises a gas pipeline multi-dimensional parameter sensing module, an original data cleaning module, a gas pipeline situation assessment module, a gas pipeline pressure compensation demand determination module, a self-adaptive pressure compensation regulation and control module, and a monitoring and management terminal. The system acquires multi-dimensional physical quantities of the gas pipeline and processes them to output clean and regular data streams. The gas pipeline situation assessment module actively senses and accurately determines the operation safety of the pipeline. The gas pipeline pressure compensation demand determination module reasonably determines the pressure compensation timing in combination with the situation assessment results. The self-adaptive pressure compensation regulation and control module dynamically determines the optimal speed of the pressure compensation pump and automatically compensates the pressure. The pressure compensation execution monitoring module verifies and evaluates the pressure compensation effect to ensure that the control instructions are effectively implemented, thereby comprehensively ensuring the stable pressure and safe operation of the gas pipeline.

Description

Technical Field

[0001] This invention relates to the field of gas pipeline control technology, specifically to an adaptive pressure compensation control system for gas pipelines based on Internet of Things (IoT) monitoring technology. Background Technology

[0002] As a core infrastructure for urban energy supply, the pressure stability of gas pipelines directly affects the safety and continuity of gas supply for residents' daily use and industrial production. With the large-scale extension of gas pipeline networks, dynamic fluctuations in gas load (such as peak gas consumption in the morning and evening, seasonal changes), and real-time changes in ambient temperature, the traditional pressure replenishment mode that relies on manual inspection and fixed threshold triggering is no longer suitable for complex and ever-changing operating conditions. There is an urgent need to use intelligent technology to achieve accurate pressure sensing, dynamic decision-making, and adaptive control.

[0003] Currently, although the field of gas pipeline pressure control has gradually transformed towards intelligence, gas pipeline pressure replenishment technologies mostly focus on monitoring a single pressure parameter and generally use fixed pressure thresholds to trigger pressure replenishment actions. They do not combine dynamic factors to build a flexible target pressure system, which leads to contradictions such as excessive pressure replenishment causing pipeline pressure surges or insufficient pressure replenishment failing to meet gas supply demand.

[0004] Furthermore, there is a lack of dynamic situation assessment mechanism for the safe operation of gas pipelines. Blindly implementing automatic pressure replenishment without identifying risks can easily lead to safety hazards. In addition, there is a general lack of reasonable verification and evaluation mechanism for the pressure replenishment effect. The final pressure value after pressure replenishment is used to judge whether the standard is met, without paying attention to the rationality and stability of the pressure recovery trajectory. This makes it difficult to ensure the continuity and reliability of pressure replenishment control, which is not conducive to ensuring the pressure stability and operational safety of gas pipelines.

[0005] To address the aforementioned technical shortcomings, a solution is proposed. Summary of the Invention

[0006] The purpose of this invention is to provide an adaptive pressure compensation control system for gas pipelines based on Internet of Things (IoT) monitoring technology, so as to solve the technical defects mentioned in the background art.

[0007] To achieve the above objectives, the present invention provides the following technical solution: an adaptive pressure replenishment control system for gas pipelines based on Internet of Things (IoT) monitoring technology, comprising a gas pipeline multi-dimensional parameter sensing module, a raw data cleaning module, a gas pipeline status assessment module, a gas pipeline pressure replenishment demand determination module, an adaptive pressure replenishment control module, and a monitoring and management terminal;

[0008] The gas pipeline multi-dimensional parameter sensing module monitors the gas pipeline, collects multi-dimensional physical quantities of the gas pipeline and outputs raw data streams, and the raw data cleaning module verifies, repairs and standardizes the raw data streams.

[0009] The gas pipeline status assessment module performs real-time assessment of the operational safety of the gas pipeline. The gas pipeline pressure replenishment demand determination module receives the gas pipeline status assessment information and analyzes the gas pipeline pressure replenishment demand. When a pressure replenishment start signal is generated, the adaptive pressure replenishment control module performs adaptive pressure replenishment of the gas pipeline and sends the pressure replenishment control information to the monitoring and management terminal for display.

[0010] Furthermore, the gas pipeline multi-dimensional parameter sensing module collects the absolute pressure value of the pipeline once per second through an embedded piezoelectric pressure sensor, monitors the instantaneous flow rate through an ultrasonic flow meter, and captures the ambient and pipe wall temperature through a temperature sensor.

[0011] Furthermore, the raw data cleaning module receives the raw data stream from the gas pipeline multi-dimensional parameter sensing module, filters out obviously erroneous data points according to the preset physical limit threshold range, and uses a time series-based linear interpolation algorithm to smooth and repair numerical abrupt changes or loss caused by brief signal interference. Finally, the data of different dimensions and magnitudes are processed into a unified numerical range through a normalization algorithm.

[0012] Furthermore, the specific analysis process of the gas pipeline status assessment module is as follows:

[0013] The system acquires the pressure values ​​of the gas pipeline at the current moment and the previous moment after cleaning, and calculates the pressure difference ΔP by comparing the pressure values ​​at the current moment and the previous moment after cleaning. It also collects the flow rate data after cleaning over a given period and calculates its standard deviation to obtain the flow rate fluctuation value Fstd. The system calculates the situation risk factor L. It then compares the situation risk factor L with the situation safety threshold Lth. If L ≥ Lth, the gas pipeline is labeled as "risky situation"; if L < Lth, the gas pipeline is labeled as "normal situation." Finally, the system sends the gas pipeline situation assessment information to the gas pipeline pressure replenishment demand determination module.

[0014] Furthermore, the specific analysis process of the gas pipeline pressurization demand determination module includes:

[0015] Obtain gas pipeline status assessment information. If a "risk status" label is received, generate an alarm message and send it to the monitoring and management terminal. Temporarily disable automatic pressure replenishment and wait for manual confirmation.

[0016] If the "normal state" label is received, the dynamic target pressure threshold Pd is obtained through target pressure dynamic decision analysis, the real-time pressure Pc of the gas pipeline is obtained, the real-time pressure Pc is compared with the dynamic target pressure threshold Pd, and when Pc is continuously lower than Pd for more than a set time, the gas pipeline is determined to be in "pressure replenishment state", a pressure replenishment start signal is generated and sent to the adaptive pressure replenishment control module.

[0017] Furthermore, the specific methods for obtaining the dynamic decision analysis of target pressure are as follows:

[0018] Obtain the basic pressure setpoint Pb for the current time period, as well as the current instantaneous flow rate Fc and the designed maximum allowable flow rate Fm of the gas pipeline; collect the current ambient temperature and the standard reference temperature, and mark the deviation of the current ambient temperature from the standard reference temperature as the temperature difference value ΔT; and calculate the dynamic target pressure threshold Pd.

[0019] Furthermore, the specific operation process of the adaptive pressure compensation and control module includes:

[0020] The real-time pressure Pc and dynamic target pressure threshold Pd of the gas pipeline are obtained, and the pressure compensation value Pj is calculated. The base speed Sb for starting the booster pump is obtained, as well as the current instantaneous flow rate Fc and rated flow rate Fr of the gas pipeline are obtained, and the optimal speed S of the booster pump is determined by calculation.

[0021] A control command containing the optimal speed S is generated and sent to the pressure compensation actuator; the pressure compensation actuator performs corresponding pressure compensation actions based on the control command, and performs adaptive pressure compensation operation on the gas pipeline.

[0022] Furthermore, the adaptive pressure compensation control module is communicatively connected to the pressure compensation execution monitoring module. The adaptive pressure compensation control module sends pressure compensation control information to the pressure compensation execution monitoring module. During the pressure compensation action, the pressure compensation execution monitoring module monitors and tracks the entire pressure compensation process and judges the pressure compensation execution effect. Based on this, it generates a pressure compensation execution early warning signal or a pressure compensation execution qualified signal and sends the pressure compensation execution early warning signal or the pressure compensation execution qualified signal to the monitoring and management terminal. When the monitoring and management terminal receives the pressure compensation execution early warning signal, it issues a corresponding early warning.

[0023] Furthermore, the specific operation process of the pressure compensation execution monitoring module is as follows:

[0024] Immediately after the pressure replenishment action is initiated, the system enters monitoring mode. When the pressure replenishment action ends, it determines whether the actual stable pressure of the gas pipeline is within the allowable error of the dynamic target pressure threshold Pd. If the actual stable pressure of the gas pipeline is not within the allowable error of the dynamic target pressure threshold Pd, a pressure replenishment execution warning signal is generated.

[0025] Furthermore, if the actual stable pressure of the gas pipeline is within the allowable error of the dynamic target pressure threshold Pd, then the expected pressure recovery curve and the actual pressure recovery curve are plotted, and a rectangular coordinate system is established with time as the X-axis and pressure as the Y-axis. The expected pressure recovery curve and the actual pressure recovery curve are respectively placed in the first quadrant of the rectangular coordinate system, and the starting points of the expected pressure recovery curve and the actual pressure recovery curve are both located on the Y-axis.

[0026] Based on the expected pressure recovery curve and the actual pressure recovery curve, the non-overlapping ratio between the two is calculated and marked as the curve trajectory intersection value. The curve trajectory intersection value is compared with a preset curve trajectory intersection threshold. If the curve trajectory intersection value exceeds the preset curve trajectory intersection threshold, a pressure replenishment execution warning signal is generated; if the curve trajectory intersection value does not exceed the preset curve trajectory intersection threshold, the pressure recovery anomaly coefficient is obtained through analysis. The pressure recovery anomaly coefficient is compared with a preset pressure recovery anomaly coefficient threshold. If the pressure recovery anomaly coefficient exceeds the preset pressure recovery anomaly coefficient threshold, a pressure replenishment execution warning signal is generated; if the pressure recovery anomaly coefficient does not exceed the preset pressure recovery anomaly coefficient threshold, a pressure replenishment execution qualified signal is generated.

[0027] Furthermore, the specific method for analyzing and obtaining the anomaly coefficient of pressure recovery is as follows:

[0028] Select several coordinate points with equal time intervals on the X-axis, draw rays upward from the selected coordinate points as endpoints, and mark the drawn rays as target rays; mark the intersection of the target ray and the expected pressure recovery curve as the expected protrusion point, mark the intersection of the target ray and the actual pressure recovery curve as the actual protrusion point, and mark the Y-axis distance between the expected protrusion point and the actual protrusion point as the misalignment coefficient.

[0029] All misalignment coefficients are obtained and their average values ​​are calculated to obtain the misalignment characteristic value. The misalignment coefficient with the largest value is marked as the misalignment amplitude measurement value. The pressure recovery anomaly coefficient is obtained by weighted summation of the curve trajectory intersection value, the misalignment characteristic value and the misalignment amplitude measurement value.

[0030] Compared with the prior art, the beneficial effects of the present invention are:

[0031] 1. In this invention, multidimensional physical quantities of gas pipelines are collected and processed to output a clean and regular data stream. The pipeline operation safety is actively perceived and accurately judged. Combined with the situation assessment results, the timing of pressure replenishment is reasonably determined. The adaptive pressure replenishment control module dynamically determines the optimal speed of the pressure replenishment pump and automatically replenishes pressure, realizing flexible and precise pressure replenishment, which is conducive to ensuring the stability of gas pipeline pressure and operation safety.

[0032] 2. In this invention, the pressure replenishment execution monitoring module performs reasonable verification and evaluation of the pressure replenishment effect based on pressure compliance verification, curve trajectory comparison and pressure recovery anomaly analysis, and generates early warning signals or qualified feedback in a timely manner to ensure that control commands are effectively implemented, forming a complete control chain of "perception-decision-execution-verification", and further ensuring the safe and stable operation of gas pipelines. Attached Figure Description

[0033] To facilitate understanding by those skilled in the art, the present invention will be further described below with reference to the accompanying drawings;

[0034] Figure 1 This is a system block diagram of Embodiment 1 of the present invention;

[0035] Figure 2 This is a system block diagram of Embodiment 2 of the present invention. Detailed Implementation

[0036] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0037] Example 1: As Figure 1 As shown, the adaptive pressure replenishment control system for gas pipelines based on Internet of Things monitoring technology proposed in this invention includes a gas pipeline multi-dimensional parameter sensing module, a raw data cleaning module, a gas pipeline status assessment module, a gas pipeline pressure replenishment demand determination module, an adaptive pressure replenishment control module, and a monitoring and management terminal.

[0038] The gas pipeline multi-dimensional parameter sensing module monitors the gas pipeline, collects multi-dimensional physical quantities such as pressure, flow rate, and temperature of the gas pipeline at high frequency, and outputs the collected raw data stream to the raw data cleaning module.

[0039] Specifically, the gas pipeline multi-dimensional parameter sensing module collects the absolute pressure value of the pipeline once per second through an embedded piezoelectric pressure sensor, monitors the instantaneous flow rate through an ultrasonic flow meter, and captures the ambient and pipe wall temperatures through a temperature sensor. By synchronously collecting multi-dimensional heterogeneous data, it overcomes the limitations of judging a single pressure parameter and provides rich operating condition characteristic information for subsequent in-depth analysis.

[0040] The raw data cleaning module verifies, repairs, and standardizes the raw data stream, removing invalid information to ensure the reliability of subsequent analysis inputs. Specifically, after receiving the raw data stream from the gas pipeline multi-dimensional parameter sensing module, the raw data cleaning module first performs rule verification, filtering out obviously erroneous data points based on preset physical limit thresholds (such as pressure values ​​that cannot be negative or exceed pipeline design limits). Subsequently, for numerical abrupt changes or loss caused by brief signal interference, a time-series-based linear interpolation algorithm is used for smooth repair.

[0041] Finally, pressure, flow, temperature and other data of different dimensions and magnitudes are processed into a unified numerical range through a normalization algorithm. The processed clean and regular data stream is pushed to the gas pipeline status assessment module and the gas pipeline pressurization demand determination module in real time, which effectively eliminates the interference of noise and abnormal data on the analysis and improves the accuracy and stability of the entire system's decision-making.

[0042] The gas pipeline status assessment module performs real-time assessments of the operational safety of gas pipelines and sends the assessment information to the gas pipeline pressurization demand determination module. This enables proactive perception and quantitative assessment of gas pipeline safety risks, facilitating the early identification of potential risks such as leaks and blockages, shifting from reactive remediation to proactive early warning, and providing a safety context for pressurization decisions. The specific analysis process is as follows:

[0043] Obtain the pressure values ​​of the gas pipeline at the current time and the previous time after cleaning, and calculate the pressure difference ΔP by comparing the pressure values ​​at the current time and the previous time after cleaning; statistically analyze the flow data after cleaning within the duration period and calculate its standard deviation to obtain the flow fluctuation value Fstd; it should be noted that during periods of stable gas load, a significant increase in the standard deviation of flow may indicate the presence of a leak point.

[0044] The situation risk factor L is calculated using the situation assessment formula L=α×|ΔP / Δt|+β×Fstd; where Δt represents the interval duration, |ΔP / Δt| can be understood as the pressure change rate (in MPa / s, this parameter reflects the speed of abnormal pressure changes), and α and β are preset weighting coefficients.

[0045] The situation risk factor L is compared with the situation safety threshold Lth. If L≥Lth, it indicates that the probability of the gas pipeline having operational safety hazards is high, and the gas pipeline is labeled as "risk situation". If L<Lth, it indicates that the probability of the gas pipeline having operational safety hazards is low, and the gas pipeline is labeled as "normal situation".

[0046] The gas pipeline pressurization demand determination module receives gas pipeline status assessment information and analyzes the gas pipeline pressurization demand, achieving intelligent and adaptive determination of pressure demand. This ensures pressure stability, reduces ineffective actions, extends equipment life, and demonstrates a high level of intelligence and automation. The specific analysis process of the gas pipeline pressurization demand determination module is as follows:

[0047] Obtain gas pipeline status assessment information. If a "risk status" label is received, immediately adopt a stricter judgment logic, that is, generate alarm information and send it to the monitoring and management terminal, and temporarily prohibit automatic pressure replenishment and wait for manual confirmation.

[0048] If the “normal situation” label is received, the basic pressure setpoint Pb (fixed known parameter, unit MPa) for the current time period (such as weekday peak and nighttime low) is obtained, as well as the current instantaneous flow rate Fc of the gas pipeline (provided by the raw data cleaning module, unit m³ / h) and the designed maximum allowable flow rate Fm (fixed known parameter, unit m³ / h).

[0049] It also collects the current ambient temperature and the standard reference temperature, and marks the deviation of the current ambient temperature from the standard reference temperature as the temperature difference value ΔT (calculated from the temperature data provided by the raw data cleaning module, in °C, used to compensate for the influence of temperature on gas volume and pipeline stress).

[0050] The dynamic target pressure threshold Pd is calculated using the formula Pd=Pb+γ×(Fc / Fm)+δ×ΔT, where γ is the preset flow compensation coefficient and δ is the preset temperature compensation coefficient. The real-time pressure Pc of the gas pipeline is obtained and compared with the dynamic target pressure threshold Pd. When Pc is continuously lower than Pd for more than a set time (e.g., 30 seconds), the gas pipeline is determined to be in a "pressure replenishment state", a pressure replenishment start signal is generated and sent to the adaptive pressure replenishment control module.

[0051] After receiving the pressure replenishment start signal from the gas pipeline pressure replenishment demand determination module, the adaptive pressure replenishment control module generates precise control commands to adaptively replenish the gas pipeline. This facilitates refined and flexible control of the gas pipeline pressure replenishment process, avoids pressure surges, achieves stable pressure replenishment, and sends the pressure replenishment control information to the monitoring and management terminal for display, allowing managers to have a detailed understanding of the automatic pressure replenishment status. The specific operation process of the adaptive pressure replenishment control module is as follows:

[0052] The core task is to determine the optimal speed of the booster pump: obtain the real-time pressure Pc and dynamic target pressure threshold Pd of the gas pipeline, and calculate the pressure compensation distance Pj using Pj=Pd-Pc; obtain the base speed Sb (preset constant) for starting the booster pump, and obtain the current instantaneous flow rate Fc and rated flow rate Fr of the gas pipeline (fixed known parameters, unit m³ / h), and calculate the optimal speed S of the booster pump using the formula S=Sb+k×Pj×(1+Fc / Fr); where k is a preset proportional adjustment coefficient, set according to the specific pump characteristics;

[0053] This formula enables the pressure replenishment intensity to adaptively adjust according to the size of the pressure gap and the current flow load, ultimately generating a control command containing the optimal speed S, and sending the control command to the pressure replenishment actuator; the pressure replenishment actuator performs corresponding pressure replenishment actions based on the control command, and performs adaptive pressure replenishment operation on the gas pipeline.

[0054] Example 2: Figure 2 As shown, the difference between this embodiment and Embodiment 1 is that the adaptive pressure control module is communicatively connected to the pressure compensation execution monitoring module. The adaptive pressure control module sends pressure compensation control information to the pressure compensation execution monitoring module. During the pressure compensation action, the pressure compensation execution monitoring module monitors and tracks the entire pressure compensation process and judges the pressure compensation execution effect, thereby generating a pressure compensation execution early warning signal or a pressure compensation execution qualified signal.

[0055] Furthermore, the pressure compensation execution early warning signal or pressure compensation execution qualified signal is sent to the monitoring and management terminal. When the monitoring and management terminal receives the pressure compensation execution early warning signal, it issues a corresponding early warning, realizing closed-loop verification and protection of the pressure compensation execution result, ensuring the effective implementation of control commands, and forming a complete "perception-decision-execution-verification" control loop; the specific operation process is as follows:

[0056] Immediately after the pressure replenishment action is initiated, the system enters monitoring mode. When the pressure replenishment action ends, it determines whether the actual stable pressure of the gas pipeline is within the allowable error of the dynamic target pressure threshold Pd. If the actual stable pressure of the gas pipeline is not within the allowable error of the dynamic target pressure threshold Pd, it indicates that the final pressure replenishment has not met the target, and a pressure replenishment execution warning signal is generated.

[0057] If the actual pressure of the gas pipeline after stabilization is within the allowable error of the dynamic target pressure threshold Pd, it indicates that the final pressure replenishment has met the standard. Then, draw the expected pressure recovery curve and the actual pressure recovery curve, and establish a rectangular coordinate system with time as the X-axis and pressure as the Y-axis. Place the expected pressure recovery curve and the actual pressure recovery curve in the first quadrant of the rectangular coordinate system, and the starting points of the expected pressure recovery curve and the actual pressure recovery curve are both located on the Y-axis.

[0058] Based on the expected pressure recovery curve and the actual pressure recovery curve, the non-overlapping ratio between the two is calculated and marked as the curve trajectory intersection value. The larger the curve trajectory intersection value, the more abnormal the pressure recovery in the gas pipeline. The curve trajectory intersection value is compared with the preset curve trajectory intersection threshold. If the curve trajectory intersection value exceeds the preset curve trajectory intersection threshold, it indicates that the pressure recovery in the gas pipeline is abnormal, and a pressure replenishment execution warning signal is generated.

[0059] If the curve trajectory intersection value does not exceed the preset curve trajectory intersection threshold, then select several coordinate points with equal time intervals on the X-axis, draw a ray upward from the selected coordinate points as endpoints, and mark the drawn ray as the target ray; mark the intersection point of the target ray and the expected pressure recovery curve as the expected protrusion point, mark the intersection point of the target ray and the actual pressure recovery curve as the actual protrusion point, and mark the Y-axis distance value between the expected protrusion point and the actual protrusion point as the misalignment coefficient;

[0060] All misalignment coefficients are obtained and their averages are calculated to obtain the misalignment characteristic value. The misalignment coefficient with the largest value is marked as the misalignment amplitude measurement value. The pressure recovery anomaly coefficient is obtained by weighted summation of the curve trajectory intersection value, the misalignment characteristic value, and the misalignment amplitude measurement value. That is, the curve trajectory intersection value, the misalignment characteristic value, and the misalignment amplitude measurement value are each assigned a corresponding preset weight coefficient, and the curve trajectory intersection value, the misalignment characteristic value, and the misalignment amplitude measurement value are multiplied by the corresponding preset weight coefficient, and the sum of the three sets of product results is marked as the pressure recovery anomaly coefficient.

[0061] It should be noted that the larger the value of the pressure recovery anomaly coefficient, the worse the overall performance of the pressure replenishment execution. The pressure recovery anomaly coefficient is compared with the preset pressure recovery anomaly coefficient threshold. If the pressure recovery anomaly coefficient exceeds the preset pressure recovery anomaly coefficient threshold, it indicates that the overall performance of the pressure replenishment execution is poor, and a pressure replenishment execution warning signal is generated. If the pressure recovery anomaly coefficient does not exceed the preset pressure recovery anomaly coefficient threshold, it indicates that the overall performance of the pressure replenishment execution is good, and a pressure replenishment execution qualified signal is generated.

[0062] The working principle of this invention is as follows: During use, the gas pipeline multi-dimensional parameter sensing module collects multi-dimensional physical quantities of the gas pipeline; the raw data cleaning module processes the collected data to output a clean and orderly data stream; the gas pipeline situation assessment module actively senses and accurately judges the pipeline operation safety, identifying potential risks such as leaks and blockages in advance; the gas pipeline pressure replenishment demand judgment module accurately judges the timing of pressure replenishment based on the situation assessment results, ensuring pressure stability while avoiding safety hazards; the adaptive pressure replenishment control module dynamically determines the optimal speed of the pressure replenishment pump, achieving flexible and precise pressure replenishment and avoiding pipeline damage caused by pressure shocks; and the pressure replenishment execution monitoring module verifies and evaluates the pressure replenishment effect to ensure that control commands are effectively implemented, comprehensively ensuring the stability of gas pipeline pressure and operational safety, and significantly improving the intelligence and precision of gas pipeline control.

[0063] In this invention, the threshold, preset value, or preset range settings are for result comparison and analysis to determine whether the result is good or bad. The magnitude of these values ​​is determined by a combination of large-scale model analysis of sample data and human experience, and can also be appropriately adjusted based on seasonal or common-sense influence conditions. Similarly, the preset weight coefficients and influence factors are assigned specific values ​​based on the magnitude of each parameter's influence on the result, ultimately reflecting the impact on the result. These settings are also determined by a combination of large-scale model analysis of sample data and human experience, and can also be appropriately adjusted based on seasonal or common-sense influence conditions.

[0064] The preferred embodiments of the present invention disclosed above are merely illustrative of the invention. These preferred embodiments do not exhaustively describe all details, nor do they limit the invention to any specific implementation. Clearly, many modifications and variations can be made based on the content of this specification. This specification selects and specifically describes these embodiments to better explain the principles and practical applications of the invention, enabling those skilled in the art to better understand and utilize it. The invention is limited only by the claims and their full scope and equivalents.

Claims

1. A gas pipeline adaptive pressure compensation control system based on Internet of Things (IoT) monitoring technology, characterized in that, It includes a gas pipeline multi-dimensional parameter sensing module, a raw data cleaning module, a gas pipeline status assessment module, a gas pipeline pressure replenishment demand determination module, an adaptive pressure replenishment control module, and a monitoring and management terminal; The gas pipeline multi-dimensional parameter sensing module monitors the gas pipeline, collects multi-dimensional physical quantities of the gas pipeline and outputs raw data streams, and the raw data cleaning module verifies, repairs and standardizes the raw data streams. The gas pipeline status assessment module performs real-time assessment of the operational safety of the gas pipeline. The gas pipeline pressure replenishment demand determination module receives the gas pipeline status assessment information and analyzes the gas pipeline pressure replenishment demand. When a pressure replenishment start signal is generated, the adaptive pressure replenishment control module performs adaptive pressure replenishment of the gas pipeline and sends the pressure replenishment control information to the monitoring and management terminal for display. The specific operation process of the adaptive pressure compensation and control module includes: The real-time pressure Pc and dynamic target pressure threshold Pd of the gas pipeline are obtained, and the pressure compensation value Pj is calculated by Pj=Pd-Pc; the base speed Sb for starting the pressure booster pump is obtained, as well as the current instantaneous flow rate Fc and rated flow rate Fr of the gas pipeline are obtained, and the optimal speed S of the pressure booster pump is determined by the formula S=Sb+k×Pj×(1+Fc / Fr); where k is a preset proportional adjustment coefficient. This allows the pressure replenishment intensity to adaptively adjust according to the size of the pressure gap and the current flow load, ultimately generating a control command containing the optimal speed S, and sending the control command to the pressure replenishment actuator; the pressure replenishment actuator performs corresponding pressure replenishment actions based on the control command, and performs adaptive pressure replenishment operation on the gas pipeline.

2. The adaptive pressure compensation control system for gas pipelines based on Internet of Things monitoring technology according to claim 1, characterized in that, The gas pipeline multi-dimensional parameter sensing module collects the absolute pressure value of the pipeline once per second through an embedded piezoelectric pressure sensor, monitors the instantaneous flow rate through an ultrasonic flow meter, and captures the ambient and pipe wall temperature through a temperature sensor. The raw data cleaning module filters out obviously erroneous data points based on a preset physical limit threshold range. For numerical mutations or loss, it uses a time series-based linear interpolation algorithm for smooth repair. Finally, it processes data of different dimensions and magnitudes into a unified numerical range through a normalization algorithm.

3. The adaptive pressure compensation control system for gas pipelines based on Internet of Things monitoring technology according to claim 1, characterized in that, The specific analysis process of the gas pipeline status assessment module is as follows: The system obtains the pressure values ​​of the gas pipeline at the current moment and the previous moment after cleaning, and calculates the pressure difference ΔP by comparing the pressure values ​​at the current moment and the previous moment after cleaning. It also collects the flow data after cleaning within a certain time period and calculates its standard deviation to obtain the flow fluctuation value Fstd. The system calculates the situation risk factor L. If L≥Lth, the system assigns a "risk situation" label to the gas pipeline. If L<Lth, the system assigns a "normal situation" label to the gas pipeline. The system also sends the gas pipeline situation assessment information to the gas pipeline pressure replenishment demand determination module.

4. The adaptive pressure compensation control system for gas pipelines based on Internet of Things monitoring technology according to claim 3, characterized in that, The specific analysis process of the gas pipeline pressurization requirement determination module includes: The system acquires gas pipeline status assessment information. If a "risk status" label is received, an alarm message is generated and sent to the monitoring and management terminal. Automatic pressure replenishment is temporarily disabled and manual confirmation is required. If a "normal status" label is received, the system obtains the dynamic target pressure threshold Pd through target pressure dynamic decision analysis and acquires the real-time pressure Pc of the gas pipeline. When Pc is continuously lower than Pd for more than a set time, the gas pipeline is determined to be in a "pressure replenishment state". A pressure replenishment start signal is generated and sent to the adaptive pressure replenishment control module.

5. The adaptive pressure compensation control system for gas pipelines based on Internet of Things monitoring technology according to claim 4, characterized in that, The specific methods for obtaining the data in the dynamic decision analysis of target pressure are as follows: Obtain the basic pressure setpoint Pb for the current time period, as well as the current instantaneous flow rate Fc and the designed maximum allowable flow rate Fm of the gas pipeline; collect the current ambient temperature and the standard reference temperature, and mark the deviation of the current ambient temperature from the standard reference temperature as the temperature difference value ΔT; calculate the dynamic target pressure threshold Pd.

6. The adaptive pressure compensation control system for gas pipelines based on Internet of Things monitoring technology according to claim 1, characterized in that, The adaptive pressure compensation control module is connected to the pressure compensation execution monitoring module. During the pressure compensation action, the pressure compensation execution monitoring module monitors and tracks the entire pressure compensation process and judges the pressure compensation execution effect. It also sends the pressure compensation execution early warning signal or the pressure compensation execution qualified signal to the monitoring and management terminal.

7. The adaptive pressure compensation control system for gas pipelines based on Internet of Things monitoring technology according to claim 6, characterized in that, The specific operation process of the pressure compensation execution monitoring module is as follows: When the pressure replenishment action ends, if the actual stable pressure of the gas pipeline is not within the allowable error of the dynamic target pressure threshold Pd, a pressure replenishment execution warning signal will be generated.

8. The adaptive pressure compensation control system for gas pipelines based on Internet of Things monitoring technology according to claim 7, characterized in that, If the actual stable pressure of the gas pipeline is within the allowable error of the dynamic target pressure threshold Pd, then the non-overlapping ratio between the expected pressure recovery curve and the actual pressure recovery curve is calculated and marked as the curve trajectory intersection value. If the curve trajectory intersection value exceeds the preset curve trajectory intersection threshold, a pressure replenishment execution early warning signal is generated. If the curve trajectory intersection value does not exceed the preset curve trajectory intersection threshold, the pressure recovery anomaly coefficient is obtained through analysis. If the pressure recovery anomaly coefficient exceeds the preset pressure recovery anomaly coefficient threshold, a pressure replenishment execution warning signal is generated; otherwise, a pressure replenishment execution qualified signal is generated.

9. The adaptive pressure compensation control system for gas pipelines based on Internet of Things monitoring technology according to claim 8, characterized in that, The specific method for analyzing and obtaining the anomaly coefficient of pressure rebound is as follows: All misalignment coefficients are obtained and their average values ​​are calculated to obtain the misalignment characteristic value. The misalignment coefficient with the largest value is marked as the misalignment amplitude measurement value. The pressure recovery anomaly coefficient is obtained by weighted summation of the curve trajectory intersection value, the misalignment characteristic value and the misalignment amplitude measurement value.

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