A natural gas station intelligent distribution method based on the Internet of Things

Abstract

The invention discloses an intelligent distribution method for a natural gas station based on the Internet of Things, and relates to the technical field of distribution design for natural gas stations. The invention obtains flow, pressure and valve status data in real time through an Internet of Things sensor, uses a weighted average method and a Kalman filter to perform data fusion and optimization, and simultaneously judges the relative position relationship between a flow meter and a regulating valve in real time, dynamically identifies changes in branch configuration, and triggers an adaptive branch matching logic to adjust the control logic; at the same time, a PID control valve opening is adopted, and adaptive dead zone compensation is performed in combination with the historical operation data of the valve and the current state, so as to eliminate valve response hysteresis, improve regulation accuracy, and reduce flow fluctuation; in addition, in the feedback adjustment stage, the adjustment result is continuously monitored, the actual flow and pressure values ​​are compared with the target values, and the valve opening is dynamically adjusted through real-time error calculation to keep the output consistent with the target.

Description

Technical Field

[0001] The present invention relates to the technical field of natural gas station distribution design, and in particular to a natural gas station intelligent distribution method based on the Internet of Things. Background Art

[0002] The main task of the natural gas station distribution system is to achieve dynamic adjustment of pressure and flow according to the needs of different users, to ensure the stability and efficiency of gas supply. As the demand for natural gas becomes more diversified and complicated, some distribution systems have gradually introduced automated control technology, relying on programmable logic controllers to perform basic adjustments to valve opening, flow and pressure, in order to improve regulation efficiency.

[0003] However, in the metering and pressure regulation segmented scenario, since some stations adopt flexible branch connection design, flow meters and regulating valves are distributed in different branches. The traditional automatic distribution system is based on the preset overall branch operation logic and cannot adapt to the changes in branch combination, resulting in system failure when the actual gas transmission branch combination changes, and the regulation logic cannot match the on-site working conditions, affecting the distribution efficiency and gas supply stability; in addition, the traditional system has the problem of uneven response during algorithm switching. When different control algorithms are in regulation mode, they cannot effectively connect the current valve position signal with the new algorithm output, resulting in an instantaneous sudden change in valve opening, causing drastic fluctuations in flow or pressure. The system is prone to oscillation and overshoot, affecting gas transmission stability; therefore, there is an urgent need for an intelligent distribution method for natural gas stations based on the Internet of Things to solve such problems. Summary of the invention

[0004] In view of the above existing problems, the present invention is proposed.

[0005] The present invention provides a natural gas station intelligent distribution method based on the Internet of Things to solve the problems of poor adaptability and uneven response in the traditional natural gas station distribution method in terms of metering and pressure regulation segmentation and algorithm switching, and it is difficult to meet the current complex working conditions and the needs of efficient operation.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0007] The embodiment of the present invention provides a natural gas station intelligent distribution method based on the Internet of Things, which includes:

[0008] Step S1, deploying IoT sensors in the natural gas station to collect distribution data in the station distribution process in real time, including flow, pressure and valve operation status;

[0009] Step S2, based on the collected distribution data, the opening of the regulating valve is adjusted through the programmable logic controller PLC, and the distribution control is performed according to the preset target flow and pressure;

[0010] Step S3, continuously monitoring the adjustment result of step S2, and comparing the actual flow and pressure values ​​after adjustment with the target values ​​in real time, and adjusting the opening of the regulating valve;

[0011] In step S3, when it is detected that the instantaneous flow exceeds the safety threshold, the temporary constant flow regulation mode is automatically triggered; under the temporary constant flow regulation, the pressure control is replaced by flow control to suppress the risk of flow surge;

[0012] In step S3, the overall operating conditions of the station are analyzed in real time, including the current flow trend, pressure fluctuation amplitude and valve response status, to determine whether the control mode needs to be switched; when switching the control mode, in order to prevent sudden changes in the valve position signal, an output following mechanism is introduced, and the current valve opening is used as the initial value of the new control algorithm to ensure the continuity of regulation during the switching process and avoid instantaneous overshoot or system oscillation.

[0013] As a preferred solution of the method for intelligent distribution and transmission of natural gas stations based on the Internet of Things described in the present invention, wherein: in step S1, the distribution and transmission data are fused, and based on the fused distribution and transmission data, the relative position relationship between the current flow meter and the regulating valve is identified to determine whether the branch configuration has changed;

[0014] When dynamic changes in the branch are detected, the adaptive branch matching logic is automatically triggered to adjust the control logic in real time to keep the flow data consistent with the pressure feedback.

[0015] As a preferred solution of the method for intelligent distribution of natural gas stations based on the Internet of Things described in the present invention, the step of identifying the relative position relationship between the current flow meter and the regulating valve and judging whether the branch configuration has changed is as follows:

[0016] The flow, pressure and valve status data are integrated and optimized based on the weighted average method and Kalman filter. The weighted average fusion formula is:

[0017] ,

[0018] in, Indicates A flow sensor at a time The collected traffic data, Indicates The pressure sensor is The collected pressure data, Indicates A valve status sensor at time The valve status data collected, Indicates The weight coefficient of each sensor satisfies , Represents the fused flow, pressure and valve status data respectively,

[0019] The Kalman filter is used to further optimize the fusion data, and the optimization formula is:

[0020] ,

[0021] ,

[0022] in, Indicates time The state estimation vector under Indicates time The observation vector under, including flow, pressure and valve status data, represents the observation matrix, Indicates time The Kalman gain matrix under Indicates time The covariance matrix of represents the identity matrix;

[0023] Identify the relative position relationship between the flow meter and the regulating valve, and describe the relative position relationship between the flow meter and the regulating valve by the degree of deviation. The calculation formula for the degree of deviation is:

[0024] ,

[0025] in, Indicates time The degree of deviation between the lower flow meter and the regulating valve, represents the fused traffic data, Indicates the fused valve status data, Indicates the proportionality coefficient, which indicates the relationship between valve opening and flow. Exceeding the threshold When the branch configuration changes dynamically.

[0026] As a preferred solution of the method for intelligent distribution of natural gas stations based on the Internet of Things described in the present invention, the step of adjusting the control logic in real time to keep the flow data consistent with the pressure feedback is:

[0027] Automatically trigger the adaptive branch matching logic and define the error objective function as , the objective function formula is:

[0028] ,

[0029] in, represents the control error objective function, Indicates target flow and target pressure, Represents the weight coefficient, which controls the influence of flow error and pressure error on the objective function respectively.

[0030] Real-time adjustment of control parameters through gradient descent method , the adjustment formula is:

[0031] ,

[0032] in, Indicates time The control parameters under represents the learning rate,

[0033] Represents the objective function Parameters The partial derivative of .

[0034] As a preferred solution of the method for intelligent distribution and transmission of natural gas stations based on the Internet of Things described in the present invention, in the process of distribution and transmission control,

[0035] At the same time, the dead zone range is adaptively compensated based on the historical operating status and current operating status of the valve.

[0036] As a preferred solution of the method for intelligent distribution of natural gas stations based on the Internet of Things described in the present invention, the steps of adjusting the opening of the regulating valve by a programmable logic controller (PLC) and performing distribution control according to preset target flow and pressure are as follows:

[0037] Based on PID control and adjustment of the valve, the control formula for defining the valve opening is:

[0038] ,

[0039] in, Indicates time The opening of the lower valve, Indicates time The flow error under , represents the proportional, integral and differential coefficients of the PID controller, represents the change in error, , Indicates the time interval, Indicates from the initial time to the time The historical error value.

[0040] As a preferred solution of the method for intelligent distribution of natural gas stations based on the Internet of Things described in the present invention, the step of adaptively compensating the dead zone range by combining the historical operating state and the current operating state of the valve is as follows:

[0041] The dead zone is compensated by the difference between the historical valve state and the current valve state, and the formula is:

[0042] ,

[0043] in, Indicates time Dead zone compensation under Indicates time and The valve status under Indicates the dead zone compensation coefficient;

[0044] The final valve opening is: .

[0045] As a preferred solution of the method for intelligent distribution of natural gas stations based on the Internet of Things described in the present invention, the step of comparing the actual flow and pressure values ​​after adjustment with the target values ​​in real time and adjusting the opening of the regulating valve is as follows:

[0046] Real-time comparison and opening adjustment to define flow error and pressure error The real-time calculation formula is:

[0047] ,

[0048] in, Indicates time Lower flow error, Indicates time Downforce error, The target flow and target pressure are preset in the table. Represents the fused real-time flow and pressure data,

[0049] When the error exceeds the threshold, the valve opening is adjusted dynamically, and the adjustment formula is:

[0050] ,

[0051] in, Indicates time The valve opening under Indicates the adjustment step coefficient of flow error and pressure error.

[0052] As a preferred solution of the method for intelligent distribution of natural gas stations based on the Internet of Things described in the present invention, when it is detected that the instantaneous flow exceeds the safety threshold, the temporary constant flow regulation mode is automatically triggered; under the temporary constant flow regulation, the step of replacing pressure control with flow control is as follows:

[0053] When instantaneous flow is detected Exceeding safety threshold When the constant current regulation mode is triggered automatically, under the constant current mode:

[0054] ,

[0055] in, represents the valve opening at the previous time step, Indicates that the pressure control authority is reset to zero, i.e. pressure regulation is replaced by constant flow.

[0056] As a preferred solution of the method for intelligent distribution of natural gas stations based on the Internet of Things described in the present invention, the step of determining whether the control mode needs to be switched is:

[0057] Perform real-time condition analysis and control mode switching, including flow trend analysis , pressure fluctuations and valve response status , the analysis formula is:

[0058] ,

[0059] in, Indicates the flow rate change, Indicates the pressure change, Indicates time The lower valve response state indicates the dynamic adjustment rate of the valve;

[0060] The conditions for judging the control mode switching are:

[0061] like or ,in It is the flow and pressure fluctuation threshold, which determines the unstable state and switches the mode;

[0062] The output following mechanism is introduced, and the following mechanism formula is:

[0063] ,

[0064] This mechanism means that the initial valve opening under the new control mode is used as the initial value of the new control algorithm, where: Indicates the initial valve opening in the new control mode, Indicates the valve opening under the current control mode; this mechanism can avoid sudden changes in valve opening, ensure smooth transition, and prevent overshoot and oscillation.

[0065] The beneficial effects of the present invention are as follows: the present invention obtains flow, pressure and valve status data in real time through Internet of Things sensors, uses weighted average method and Kalman filtering to perform data fusion and optimization, and simultaneously judges the relative position relationship between the flow meter and the regulating valve in real time, dynamically identifies branch configuration changes, and triggers adaptive branch matching logic, thereby adjusting the control logic, thereby solving the problem of system failure caused by mismatch between the flow meter and the regulating valve.

[0066] The present invention adopts PID control valve opening, combines the historical operation data of the valve with the current state to perform adaptive dead zone compensation, eliminates valve response hysteresis, improves adjustment accuracy, and reduces flow fluctuation; in the feedback adjustment stage, continuously monitors the adjustment result, compares the actual flow and pressure value with the target value, dynamically adjusts the valve opening through real-time error calculation, and keeps the output consistent with the target; in response to the risk of instantaneous flow exceeding the limit, automatically triggers a temporary constant flow adjustment mode, replaces pressure adjustment with flow control, suppresses flow surge, and ensures safe system operation.

[0067] The present invention performs working condition analysis in combination with flow trend, pressure fluctuation amplitude and valve response state when switching the dynamic control mode, and smoothly transitions to the new control mode through the output following mechanism to avoid overshoot and oscillation caused by sudden change of valve opening. BRIEF DESCRIPTION OF THE DRAWINGS

[0068] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0069] Figure 1 The figure is a flow chart of the method for intelligent distribution and transmission of natural gas stations based on the Internet of Things of the present invention. DETAILED DESCRIPTION

[0070] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the accompanying drawings.

[0071] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0072] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive with other embodiments.

[0073] Example 1, reference Figure 1 This embodiment provides a natural gas station intelligent distribution method based on the Internet of Things:

[0074] Step S1, deploying IoT sensors in the natural gas station to collect distribution data in the station distribution process in real time, including flow, pressure and valve operation status;

[0075] In step S1, the branch transmission data is integrated, and based on the integrated branch transmission data, the relative position relationship between the current flow meter and the regulating valve is identified to determine whether the branch configuration has changed;

[0076] When dynamic changes in the branch are detected, the adaptive branch matching logic is automatically triggered to adjust the control logic in real time to keep the flow data consistent with the pressure feedback;

[0077] The steps to identify the relative position relationship between the current flow meter and the regulating valve and determine whether the branch configuration has changed are:

[0078] The flow, pressure and valve status data are integrated and optimized based on the weighted average method and Kalman filter. The weighted average fusion formula is:

[0079] ,

[0080] in, Indicates A flow sensor at a time The collected traffic data, Indicates The pressure sensor is The collected pressure data, Indicates A valve status sensor at time The valve status data collected, Indicates The weight coefficient of each sensor satisfies , Represents the fused flow, pressure and valve status data respectively,

[0081] The Kalman filter is used to further optimize the fusion data, and the optimization formula is:

[0082] ,

[0083] ,

[0084] in, Indicates time The state estimation vector under Indicates time The observation vector under, including flow, pressure and valve status data, represents the observation matrix, Indicates time The Kalman gain matrix under Indicates time The covariance matrix of represents the identity matrix;

[0085] Identify the relative position relationship between the flow meter and the regulating valve, and describe the relative position relationship between the flow meter and the regulating valve by the degree of deviation. The calculation formula for the degree of deviation is:

[0086] ,

[0087] in, Indicates time The degree of deviation between the lower flow meter and the regulating valve, represents the fused traffic data, Indicates the fused valve status data, Indicates the proportionality coefficient, which indicates the relationship between valve opening and flow. Exceeding the threshold When , it is determined that the branch configuration has changed dynamically;

[0088] The steps to adjust the control logic in real time to keep the flow data consistent with the pressure feedback are:

[0089] Automatically trigger the adaptive branch matching logic and define the error objective function as , the objective function formula is:

[0090] ,

[0091] in, represents the control error objective function, Indicates target flow and target pressure, Represents the weight coefficient, which controls the influence of flow error and pressure error on the objective function respectively.

[0092] Real-time adjustment of control parameters through gradient descent method , the adjustment formula is:

[0093] ,

[0094] in, Indicates time The control parameters under represents the learning rate,

[0095] Represents the objective function Parameters The partial derivative of

[0096] Specifically, step S1 integrates the distributed data through weighted averaging and Kalman filtering to improve data accuracy, and calculates the degree of deviation between flow and valve state to identify the dynamic changes of branch configuration, and then adaptively matches branches through error objective function and gradient descent method to ensure the real-time performance of control logic.

[0097] Step S2, based on the collected distribution data, the opening of the regulating valve is adjusted through the programmable logic controller PLC, and the distribution control is performed according to the preset target flow and pressure;

[0098] During the distribution control process,

[0099] At the same time, the dead zone range is adaptively compensated based on the historical operating status and current operating status of the valve;

[0100] The opening of the regulating valve is adjusted by the programmable logic controller (PLC), and the steps for distribution control according to the preset target flow and pressure are as follows:

[0101] Based on PID control and adjustment of the valve, the control formula for defining the valve opening is:

[0102] ,

[0103] in, Indicates time The opening of the lower valve, Indicates time The flow error under , represents the proportional, integral and differential coefficients of the PID controller, represents the change in error, , Indicates the time interval, Indicates from the initial time to the time The historical error value of

[0104] Combining the historical operating status and current operating status of the valve, the steps for adaptively compensating the dead zone range are:

[0105] The dead zone is compensated by the difference between the historical valve state and the current valve state, and the formula is:

[0106] ,

[0107] in, Indicates time Dead zone compensation under Indicates time and The valve status under Indicates the dead zone compensation coefficient;

[0108] The final valve opening is: ;

[0109] Specifically, step S2 adjusts the valve opening through PID control to achieve tracking control of target flow and pressure, and effectively solves the valve response hysteresis problem through adaptive dead zone compensation to improve control accuracy and stability.

[0110] The dead zone mentioned in step S2 refers to the range in which the control valve does not respond to a small change in the input signal during the control process. It is often caused by factors such as the mechanical characteristics, wear or design of the valve.

[0111] Step S3, continuously monitoring the adjustment result of step S2, and comparing the actual flow and pressure values ​​after adjustment with the target values ​​in real time, and adjusting the opening of the regulating valve;

[0112] In step S3, when it is detected that the instantaneous flow exceeds the safety threshold, the temporary constant flow regulation mode is automatically triggered; under the temporary constant flow regulation, the pressure control is replaced by flow control to suppress the risk of flow surge;

[0113] In step S3, the overall working conditions of the station are analyzed in real time, including the current flow trend, pressure fluctuation amplitude and valve response status, to determine whether the control mode needs to be switched; when switching the control mode, in order to prevent sudden changes in the valve position signal, an output follower mechanism is introduced, and the current valve opening is used as the initial value of the new control algorithm to ensure the continuity of regulation during the switching process and avoid instantaneous overshoot or system oscillation;

[0114] Compare the actual flow and pressure values ​​after adjustment with the target values ​​in real time, and adjust the opening of the regulating valve in the following steps:

[0115] Real-time comparison and opening adjustment to define flow error and pressure error The real-time calculation formula is:

[0116] ,

[0117] in, Indicates time Lower flow error, Indicates time Downforce error, The target flow and target pressure are preset in the table. Represents the fused real-time flow and pressure data,

[0118] When the error exceeds the threshold, the valve opening is adjusted dynamically, and the adjustment formula is:

[0119] ,

[0120] in, Indicates time The valve opening under Indicates the adjustment step coefficient of flow error and pressure error;

[0121] When it is detected that the instantaneous flow exceeds the safety threshold, the temporary constant flow regulation mode is automatically triggered; under the temporary constant flow regulation, the steps of replacing pressure control with flow control are as follows:

[0122] When instantaneous flow is detected Exceeding safety threshold When the constant current regulation mode is triggered automatically, under the constant current mode:

[0123] ,

[0124] in, represents the valve opening at the previous time step, Indicates that the pressure control is reset to zero, that is, the pressure regulation is replaced by a constant flow.

[0125] The steps for determining whether the control mode needs to be switched are:

[0126] Perform real-time condition analysis and control mode switching, including flow trend analysis , pressure fluctuations and valve response status , the analysis formula is:

[0127] ,

[0128] in, Indicates the flow rate change, Indicates the pressure change, Indicates time The lower valve response state indicates the dynamic adjustment rate of the valve;

[0129] The conditions for judging the control mode switching are:

[0130] like or ,in It is the flow and pressure fluctuation threshold, which determines the unstable state and switches the mode;

[0131] The output following mechanism is introduced, and the following mechanism formula is:

[0132] ,

[0133] This mechanism means that the initial valve opening under the new control mode is used as the initial value of the new control algorithm, where: Indicates the initial valve opening in the new control mode, Indicates the valve opening in the current control mode; this mechanism can avoid instantaneous sudden changes in valve opening, ensure smooth transition, and prevent overshoot and oscillation;

[0134] Specifically, step S3 adjusts the flow rate and pressure close to the target value through real-time error calculation and feedback adjustment. When the instantaneous flow rate exceeds the safety threshold, the constant flow regulation mode is triggered to stabilize the flow rate and prevent the risk from spreading. At the same time, dynamic control mode switching is performed in combination with operating condition analysis, and an output following mechanism is introduced to effectively avoid sudden changes in valve opening, ensure the continuity and stability of regulation, and ensure the safety, efficiency and dynamic response capability of the natural gas distribution process.

[0135] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention can be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should be included in the scope of the claims of the present invention.

Claims

1. A natural gas station intelligent distribution method based on the Internet of Things, characterized by: include, Step S1, deploying IoT sensors in the natural gas station to collect distribution data in the station distribution process in real time, including flow, pressure and valve operation status; Step S2, based on the collected distribution data, the opening of the regulating valve is adjusted through the programmable logic controller PLC, and the distribution control is performed according to the preset target flow and pressure; Step S3, continuously monitoring the adjustment result of step S2, and comparing the actual flow and pressure values ​​after adjustment with the target values ​​in real time, and adjusting the opening of the regulating valve; In step S3, when it is detected that the instantaneous flow exceeds the safety threshold, the temporary constant flow regulation mode is automatically triggered; under the temporary constant flow regulation, the pressure control is replaced by the flow control; In step S3, the overall working conditions of the station are analyzed in real time, including the current flow trend, pressure fluctuation amplitude and valve response status, to determine whether the control mode needs to be switched; when switching the control mode, an output follower mechanism is introduced, and the current valve opening is used as the initial value of the new control algorithm; In step S1, the branch transmission data is integrated, and based on the integrated branch transmission data, the relative position relationship between the current flow meter and the regulating valve is identified to determine whether the branch configuration has changed; When dynamic changes in the branch are detected, the adaptive branch matching logic is automatically triggered to adjust the control logic in real time to keep the flow data consistent with the pressure feedback; The step of identifying the relative position relationship between the current flow meter and the regulating valve and determining whether the branch configuration has changed is: The flow, pressure and valve status data are integrated and optimized based on the weighted average method and Kalman filter. The weighted average fusion formula is: ， in, Indicates A flow sensor at a time The collected traffic data, Indicates The pressure sensor is The collected pressure data, Indicates A valve status sensor at time The valve status data collected, Indicates The weight coefficient of each sensor satisfies , Represents the fused flow, pressure and valve status data respectively, The Kalman filter is used to further optimize the fusion data, and the optimization formula is: ， ， in, Indicates time The state estimation vector under Indicates time The observation vector under, including flow, pressure and valve status data, represents the observation matrix, Indicates time The Kalman gain matrix under Indicates time The covariance matrix of represents the identity matrix; Identify the relative position relationship between the flow meter and the regulating valve, and describe the relative position relationship between the flow meter and the regulating valve by the degree of deviation. The calculation formula for the degree of deviation is: ， in, Indicates time The degree of deviation between the lower flow meter and the regulating valve, represents the fused traffic data, Indicates the fused valve status data, Indicates the proportionality coefficient, which indicates the relationship between valve opening and flow. Exceeding the threshold When , it is determined that the branch configuration has changed dynamically; The steps of comparing the actual flow and pressure values ​​after adjustment with the target values ​​in real time and adjusting the opening of the regulating valve are as follows: Real-time comparison and opening adjustment to define flow error and pressure error The real-time calculation formula is: ， in, Indicates time Lower flow error, Indicates time Downforce error, The target flow and target pressure are preset in the table. Represents the fused real-time flow and pressure data, When the error exceeds the threshold, the valve opening is adjusted dynamically, and the adjustment formula is: ， in, Indicates time The valve opening under Indicates the adjustment step coefficient of flow error and pressure error; When it is detected that the instantaneous flow exceeds the safety threshold, the temporary constant flow regulation mode is automatically triggered; under the temporary constant flow regulation, the steps of replacing the pressure control with the flow control are as follows: When instantaneous flow is detected Exceeding safety threshold When the constant current regulation mode is triggered automatically, under the constant current mode: ， in, represents the valve opening at the previous time step, Indicates that the pressure control authority is reset to zero, that is, pressure regulation is replaced by constant flow; The step of determining whether the control mode needs to be switched is: Perform real-time condition analysis and control mode switching, including flow trend analysis , pressure fluctuations and valve response status , the analysis formula is: ， in, Indicates the flow rate change, Indicates the pressure change, Indicates time The lower valve response state indicates the dynamic adjustment rate of the valve; The conditions for judging the control mode switching are: like or ,in It is the flow and pressure fluctuation threshold, which determines the unstable state and switches the mode; The output following mechanism is introduced, and the following mechanism formula is: ， This mechanism means that the initial valve opening under the new control mode is used as the initial value of the new control algorithm, where: Indicates the initial valve opening in the new control mode, Indicates the valve opening in the current control mode.

2. The method for intelligent distribution of natural gas at a natural gas station based on the Internet of Things according to claim 1, characterized in that: The steps of adjusting the control logic in real time to keep the flow data consistent with the pressure feedback are: Automatically trigger the adaptive branch matching logic and define the error objective function as , the objective function formula is: ， in, represents the control error objective function, Indicates target flow and target pressure, Represents the weight coefficient, which controls the influence of flow error and pressure error on the objective function respectively. Real-time adjustment of control parameters through gradient descent method , the adjustment formula is: ， in, Indicates time The control parameters under represents the learning rate, Represents the objective function Parameters The partial derivative of .

3. A natural gas station intelligent distribution method based on the Internet of Things as claimed in claim 2, characterized in that: During the distribution control process, At the same time, the dead zone range is adaptively compensated based on the historical operating status and current operating status of the valve.

4. The method for intelligent distribution of natural gas at a natural gas station based on the Internet of Things as claimed in claim 3, characterized in that: The steps of adjusting the opening of the regulating valve by the programmable logic controller PLC and performing distribution control according to the preset target flow and pressure are as follows: Based on PID control and adjustment of the valve, the control formula for defining the valve opening is: ， in, Indicates time The opening of the lower valve, Indicates time The flow error under , represents the proportional, integral and differential coefficients of the PID controller, represents the change in error, , Indicates a time interval.

5. The method for intelligent distribution of natural gas at a natural gas station based on the Internet of Things according to claim 4, characterized in that: The step of adaptively compensating the dead zone range by combining the historical operating state and the current operating state of the valve is: The dead zone is compensated by the difference between the historical valve state and the current valve state, the formula is: ， in, Indicates time Dead zone compensation under Indicates time and The valve status under Indicates the dead zone compensation coefficient; The final valve opening is: .

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