An automated gas station filling system

Abstract

The present invention provides an automated gas station filling system, and relates to the technical field of automatic filling of gas stations. First, key time series data in the filling process are collected, including filling pressure, filling speed and filling temperature. Then, a filling prediction model is constructed using a linear regression model, and the pressure, temperature and speed at the current moment are used as input to predict the filling speed of the next timestamp, thereby achieving forward-looking control of the filling process. In real-time operation, the current filling pressure, speed and temperature data are collected and input into the model. After obtaining the predicted filling speed, dynamic adjustment is performed in combination with a PID controller to ensure the smoothness and stability of the filling process. In addition, the set data threshold is used to monitor the filling safety in real time. By calculating the filling risk index and comparing it with the preset threshold, if it exceeds the safety standard, the system will automatically issue a danger warning and respond to potential risks in a timely manner.

Description

Technical Field

[0001] The invention relates to the technical field of automatic filling of gas stations, and in particular to an automatic filling system for gas stations. Background Art

[0002] Gas station automatic filling technology is an important part of modern gas filling system, and is widely used in the storage and filling process of natural gas, liquefied gas and other gases. With the continuous growth of energy demand and the improvement of environmental protection requirements, the efficiency, safety and automation of gas filling have become the focus of industry attention.

[0003] Traditional gas filling methods mostly rely on manual operation, which is not only inefficient, but also has certain safety hazards. For example, improper operation may lead to leakage, explosion and other accidents. For this reason, automatic filling technology came into being. By integrating sensors, control systems and data analysis technology, intelligent management of the filling process can be achieved. The application of sensors enables real-time monitoring of important parameters such as pressure, speed and temperature during the filling process to ensure safe and accurate filling.

[0004] The prior art has the following deficiencies:

[0005] Although the existing automatic filling systems for gas stations have made some progress in efficiency and safety, there are still some shortcomings, mainly in the ability to adapt to complex environments and dynamic changes. During the filling process, the filling pressure and temperature are changing. Existing technologies may not be flexible enough to handle complex or dynamically changing filling conditions, and it is difficult to provide accurate predictions of filling speeds. In addition, the system's early warning and response to sudden failures may not be sensitive enough, and it cannot guarantee timely handling of risks in all cases, leading to potential safety hazards. Therefore, improving the system's stability and fault response capabilities in complex environments is an important direction for further optimizing the automatic filling technology of gas stations. Summary of the invention

[0006] The object of the present invention is to provide an automated gas station filling system to solve the problems raised in the above background technology.

[0007] An automated gas station filling system, comprising:

[0008] A data acquisition module is used to obtain the filling pressure time series data, filling speed time series data and filling temperature time series data during the filling process of the gas station;

[0009] A model building module is used to build a filling model based on a linear regression model, taking the filling pressure, filling temperature and filling speed at the same timestamp as input and the filling speed at the next timestamp as a label to train the filling model;

[0010] The dynamic control module is used to collect the current filling pressure, filling speed and filling temperature of the gas station and input them into the filling model to obtain the predicted value of the filling speed and dynamically adjust the filling speed through the PID controller;

[0011] The data monitoring and early warning module is used to compare the currently collected filling pressure, filling speed, and filling temperature with the preset data thresholds, and calculate and generate the filling danger index at the current moment. If the filling danger index exceeds the preset danger threshold, the filling system will issue a danger warning.

[0012] Furthermore, the specific logic for collecting the filling pressure, filling speed and filling temperature data during the filling process is as follows:

[0013] A piezoelectric pressure sensor is installed at the inlet of the filling valve, an ultrasonic flow meter is installed in the middle of the filling pipe, and an infrared temperature sensor is installed at the inlet of the filling pipe to obtain the filling pressure, filling speed and filling temperature data during the filling process.

[0014] Furthermore, the constructed filling model expression is as follows:

[0015]

[0016] in, is the filling speed at time t+1 output by the filling model, P i is the filling pressure at time t, T t is the filling temperature at time t, V t is the filling speed at time t, t+1 is the next timestamp adjacent to timestamp t, β0, β1, β2, β3 are the parameters of model training, and ∈ is the error term.

[0017] Furthermore, the specific logic for dynamically adjusting the filling speed through the PID controller is:

[0018] Compare the predicted value of filling speed, the current filling pressure and filling temperature of the gas station with the preset optimal filling speed, optimal filling pressure and optimal filling temperature, respectively, and calculate the filling error term. The calculation formula is:

[0019]

[0020] Where n is the current moment, n+Δt is the future moment with a time interval of Δt from the current moment, e(Δt) represents the filling error term at the future moment n+Δt, P(n) is the current filling pressure of the gas station, is the preset optimal filling pressure, T(n) is the current filling temperature of the gas station, is the preset optimal filling temperature value, V(n+Δt) is the predicted filling speed value at the future time n+Δt output by the filling model, is the preset optimal filling speed, ω1, ω2, ω3 are weight coefficients, ω1>ω2>ω3>0, and ω1+ω2+ω3=1;

[0021]

[0022] Among them, u(t) is the filling speed adjustment at the future time n+Δt, K p , K i , K d They are the control parameters of proportion, integration and differentiation respectively.

[0023] Furthermore, the specific logic for generating the filling risk index is:

[0024] The filling pressure is compared with the preset filling pressure threshold range. If the filling pressure is within the preset filling pressure threshold range, D P =0, if the filling pressure is lower than the preset minimum filling pressure threshold, the formula for obtaining the filling pressure hazard index is:

[0025]

[0026] Among them, D P is the filling pressure hazard index, P(n) is the filling pressure at the current moment, P min is the minimum value of the filling pressure threshold range;

[0027] If the filling pressure is greater than the preset maximum filling pressure threshold value, the formula for obtaining the filling pressure hazard index is:

[0028]

[0029] Among them, P max is the maximum value of the filling pressure threshold range;

[0030] Similarly, obtain the filling speed hazard index D V , Filling temperature hazard index D T ;

[0031] The formula for obtaining the filling risk index at the current moment is:

[0032] D=λ1·D P +λ2·DV+λ3·D T

[0033] Where D is the filling risk index at the current moment, λ1, λ2, and λ3 are risk weight indexes, λ1>λ2>λ3>0, and λ1+λ2+λ3=1;

[0034] Among them, the threshold range of filling pressure is [0.2, 1.5], the unit is MPa, the threshold range of filling speed is [10, 100], the unit is L / min, the threshold unit of filling temperature is [5, 60], the unit is ℃. If the filling hazard index at the current moment is higher than the preset hazard threshold, the filling system will issue a hazard warning. If the filling hazard index at the current moment is lower than the preset hazard threshold, the filling system will continue to be monitored.

[0035] In the above technical solution, the technical effects and advantages provided by the present invention are:

[0036] The automatic filling method of the gas station realizes real-time monitoring and dynamic adjustment of the filling process by deploying sensors and adopting advanced data analysis and control technologies, significantly improving the filling efficiency and safety. Combined with the linear regression model and PID controller, the intelligent adjustment of the filling speed is realized, making the filling process more stable and accurate, reducing the risks brought by human operation, and thus significantly improving the filling efficiency and safety. In addition, the real-time early warning mechanism ensures timely response in abnormal situations, further enhancing the reliability and safety of the system. This automatic filling method not only improves the filling efficiency of the gas station, but also enhances the safety of the system. Through intelligent monitoring and dynamic adjustment, it effectively prevents possible safety hazards. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] Figure 1 It is a schematic diagram of the system structure of the present invention. DETAILED DESCRIPTION

[0038] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with specific embodiments.

[0039] It should be noted that, unless otherwise defined, the technical terms or scientific terms used in the present invention should be understood by people with ordinary skills in the field to which the present invention belongs. The words "first", "second" and similar words used in the present invention do not indicate any order, quantity or importance, but are only used to distinguish different components. "Include" or "comprise" and similar words mean that the elements or objects appearing before the word include the elements or objects listed after the word and their equivalents, without excluding other elements or objects. "Connect" or "connected" and similar words are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. "Up", "down", "left", "right" and the like are only used to indicate relative positional relationships. When the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0040] Example:

[0041] See also Figure 1 The present invention provides an automated gas station filling system, comprising:

[0042] A data acquisition module is used to obtain the filling pressure time series data, filling speed time series data and filling temperature time series data during the filling process of the gas station;

[0043] In this embodiment, the specific logic for collecting the filling pressure, filling speed and filling temperature data during the filling process is as follows:

[0044] A piezoelectric pressure sensor is installed at the inlet of the filling valve, an ultrasonic flow meter is installed in the middle of the filling pipe, and an infrared temperature sensor is installed at the inlet of the filling pipe to obtain the filling pressure, filling speed and filling temperature data during the filling process.

[0045] Gas filling involves high-pressure operations. Real-time monitoring of the filling pressure can promptly detect potential safety hazards such as leakage and overpressure, ensuring that the filling process is carried out within a safe range. Speed ​​data can help optimize the filling speed, ensuring that the filling process is fast and not out of control. Through real-time speed monitoring, it can adapt to different filling needs and environmental changes. Gas will produce temperature changes due to compression during the filling process. High temperature may cause equipment damage or changes in gas properties. Temperature monitoring can ensure that the temperature during the filling process remains within a safe range, thereby preventing equipment failures and safety accidents. Therefore, this step uses a variety of sensors to achieve comprehensive monitoring of the filling process, thereby improving the accuracy and efficiency of filling while ensuring safety.

[0046] A model building module is used to build a filling model based on a linear regression model, taking the filling pressure, filling temperature and filling speed at the same timestamp as input and the filling speed at the next timestamp as a label to train the filling model;

[0047] In this embodiment, the constructed filling model expression is as follows:

[0048]

[0049] in, is the filling speed at time t+1 output by the filling model, P i is the filling pressure at time t, T t is the filling temperature at time t, V t is the filling speed at time t, t+1 is the next timestamp adjacent to timestamp t, β0, β1, β2, β3 are the parameters of model training, and ∈ is the error term.

[0050] The filling model uses mean square error to verify the accuracy of model training. When the mean square error MSE < 1%, it indicates that the model training is completed.

[0051] The linear regression model has a small amount of calculation, fast training and prediction speed, and is suitable for the rapid processing of large-scale data sets. Through model analysis, the filling speed can be predicted under different filling conditions, providing a basis for operational decisions, thereby improving filling efficiency, identifying key factors affecting the filling speed, and optimizing operating parameters in a targeted manner to achieve more accurate filling control. And judging the quality of model training by the mean square error can clearly reflect the accuracy of model prediction. When MSE < 1%, it means that the difference between the model's prediction results and the actual values ​​is very small, indicating that the model has a high prediction accuracy on the data set, so that the output of the model can be more trusted in the decision-making process.

[0052] The dynamic control module is used to collect the current filling pressure, filling speed and filling temperature of the gas station and input them into the filling model to obtain the predicted value of the filling speed, and dynamically adjust the filling speed through the PID controller;

[0053] In this embodiment, the specific logic for dynamically adjusting the filling speed through the PID controller is:

[0054] Compare the predicted value of filling speed, the current filling pressure and filling temperature of the gas station with the preset optimal filling speed, optimal filling pressure and optimal filling temperature respectively, and calculate the filling error term. The calculation formula is:

[0055]

[0056] Where n is the current moment, n+Δt is the future moment with a time interval of Δt from the current moment, e(Δt) represents the filling error term at the future moment n+Δt, P(n) is the current filling pressure of the gas station, is the preset optimal filling pressure, T(n) is the current filling temperature of the gas station, is the preset optimal filling temperature value, V(n+Δt) is the predicted filling speed value at the future time n+Δt output by the filling model, is the preset optimal filling speed, ω1, ω2, ω3 are weight coefficients, ω1>ω2>ω3>0, and ω1+ω2+ω3=1;

[0057]

[0058] Among them, u(t) is the filling speed adjustment at the future time n+Δt, K p , K i , K d They are the control parameters of proportion, integration and differentiation respectively.

[0059] The preset filling pressure, speed and temperature values ​​can be taken from the corresponding values ​​under the best filling conditions in the historical filling data. Before filling, the pressure, temperature and other data are collected by sensors, and combined with the last filling speed, they are input into the trained filling model. This method uses historical data to predict the initial filling speed, so that the filling speed can be selected more accurately at the beginning of filling, which lays a good foundation for subsequent control. By real-time monitoring of the pressure, speed and temperature data during the filling process and comparing them with the preset values, the error term e(t) can be obtained. This error term represents the deviation between the current state and the target state and is the core of PID control. This feedback mechanism can reflect the dynamic changes of the filling process in real time and ensure the timeliness and effectiveness of the control. The weighting of the error term uses different weight coefficients, which allows optimization according to the importance of different variables during the control process. The filling pressure directly affects the safety and stability of the gas. Too high or too low pressure may cause equipment failure or safety accidents. Therefore, its importance and weight are usually the highest. Speed ​​affects filling efficiency. Although it is also related to safety (especially the problems that may arise when the filling rate is too fast), its influence is relatively small, so its weight is usually second to pressure. The influence of temperature is mainly indirect. Although it may affect the properties of the gas in extreme cases, in general, its weight is usually the lowest. The PID controller uses the ratio (K p ), integral(K i ) and differential (K d ) Three parts to process the error: proportional control, timely adjustment according to the current error; integral control, accumulation of historical errors, elimination of steady-state errors; differential control, prediction of future trends of errors, and enhancement of system responsiveness. This comprehensive control strategy can respond quickly and effectively to dynamic changes in the system, reduce overshoot and oscillation, and make the filling process smoother and more accurate.

[0060] The data monitoring and early warning module is used to compare the currently collected filling pressure, filling speed, and filling temperature with the preset data thresholds, and calculate and generate the filling danger index at the current moment. If the filling danger index exceeds the preset danger threshold, the filling system will issue a danger warning.

[0061] In this embodiment, the specific logic for generating the filling risk index is:

[0062] The filling pressure is compared with the preset filling pressure threshold range. If the filling pressure is within the preset filling pressure threshold range, D P =0, if the filling pressure is lower than the preset minimum filling pressure threshold, the formula for obtaining the filling pressure hazard index is:

[0063]

[0064] Among them, D P is the filling pressure hazard index, P(n) is the filling pressure at the current moment, P min is the minimum value of the filling pressure threshold range;

[0065] If the filling pressure is greater than the preset maximum filling pressure threshold value, the formula for obtaining the filling pressure hazard index is:

[0066]

[0067] Among them, P max is the maximum value of the filling pressure threshold range;

[0068] Similarly, obtain the filling speed hazard index D V , Filling temperature hazard index D T ;

[0069] The formula for obtaining the filling risk index at the current moment is:

[0070] D=λ1·D P +λ2·D V +λ3·D T

[0071] Where D is the filling risk index at the current moment, λ1, λ2, and λ3 are risk weight indexes, λ1>λ2>λ3>0, and λ1+λ2+λ3=1;

[0072] Among them, the threshold range of filling pressure is [0.2, 1.5], the unit is MPa, the threshold range of filling speed is [10, 100], the unit is L / min, the threshold unit of filling temperature is [5, 60], the unit is ℃. If the filling hazard index at the current moment is higher than the preset hazard threshold, the filling system will issue a hazard warning. If the filling hazard index at the current moment is lower than the preset hazard threshold, the filling system will continue to be monitored.

[0073] Through this filling hazard index, the risks in the filling process can be quantified, and warnings can be automatically issued based on deviations from preset thresholds. The formula takes into account multiple potential risk factors and derives a comprehensive hazard index by weighting. This method can more accurately reflect the comprehensive risks that may exist in the filling process, rather than considering a single factor alone. The filling pressure directly affects the safety and stability of the gas. Too high or too low pressure may lead to equipment failure or safety accidents. Therefore, its importance and weight are usually the highest. Speed ​​affects the filling efficiency. Although it is also related to safety (especially the problems that may arise when the filling rate is too fast), its impact is relatively small, so the weight is usually second to pressure. The impact of temperature is mainly indirect. Although it may affect the properties of the gas in extreme cases, in general, its weight is usually the lowest. The constraint of λ1+λ2+λ3=1 ensures that the filling hazard index is a normalized indicator, ensuring that the total contribution of the weighted combination of the three factors is 100%. The hazard threshold here can be taken as the minimum value of all filling hazard indices when dangerous events occur in historical data. Sensors are used to monitor key parameters (pressure, speed, temperature) during the filling process in real time to ensure the timeliness and accuracy of the data. This real-time monitoring can dynamically track the filling process. These thresholds are usually based on the design parameters, process requirements and safety standards of the equipment. They are the key to ensuring the safety and effectiveness of the filling process. By comparing the real-time filling hazard index with the preset threshold, the system can promptly identify any potentially dangerous situations. This abnormal detection mechanism is the basis for ensuring the stability and safety of the system. Once the monitoring data exceeds the set threshold, the system will immediately issue an early warning to remind the operator to take appropriate measures. The early warning mechanism can effectively prevent potential safety accidents or equipment damage. When all monitoring parameters are within the safe range, the system will continue to monitor to ensure real-time feedback and will not miss any abnormal situations due to monitoring suspension.

[0074] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0075] The above embodiments may be implemented in whole or in part by software, hardware, firmware or any other combination thereof. When implemented by software, the above embodiments may be implemented in whole or in part in the form of a computer program product. Those skilled in the art may appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein may be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed by hardware or software methods depends on the specific application and design constraints of the technical solution.

[0076] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, and may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0077] The above description is only a specific implementation manner of the present application, but the protection scope of the present application is not limited thereto. Any technician familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application.

Claims

1. An automated gas station filling system, characterized in that: include: A data acquisition module is used to obtain the filling pressure time series data, filling speed time series data and filling temperature time series data during the filling process of the gas station; A model building module is used to build a filling model based on a linear regression model, taking the filling pressure, filling temperature and filling speed at the same timestamp as input and the filling speed at the next timestamp as a label to train the filling model; The dynamic control module is used to collect the current filling pressure, filling speed and filling temperature of the gas station and input them into the filling model to obtain the predicted value of the filling speed and dynamically adjust the filling speed through the PID controller; The data monitoring and early warning module is used to compare the currently collected filling pressure, filling speed, and filling temperature with the preset data thresholds, and calculate the filling danger index at the current moment. If the filling danger index exceeds the preset danger threshold, the filling system will issue a danger warning; Install a piezoelectric pressure sensor at the inlet of the filling valve, an ultrasonic flow meter at the middle of the filling pipe, and an infrared temperature sensor at the inlet of the filling pipe to obtain the filling pressure, filling speed and filling temperature data during the filling process; The constructed filling model expression is as follows: in, is the filling speed at time t+1 output by the filling model, P t is the filling pressure at time t, T t is the filling temperature at time t, V t is the filling speed at time t, t+1 is the next timestamp adjacent to timestamp t, β0, β1, β2, β3 are the parameters of model training, and ∈ is the error term; Compare the predicted value of filling speed, the current filling pressure and filling temperature of the gas station with the preset optimal filling speed, optimal filling pressure and optimal filling temperature, respectively, and calculate the filling error term. The calculation formula is: Where n is the current moment, n+Δt is the future moment with a time interval of Δt from the current moment, e(Δt) represents the filling error term at the future moment n+Δt, P(n) is the current filling pressure of the gas station, is the preset optimal filling pressure, T(n) is the current filling temperature of the gas station, is the preset optimal filling temperature value, V(n+Δt) is the predicted filling speed value at the future time n+Δt output by the filling model, is the preset optimal filling speed, ω1, ω2, ω3 are weight coefficients, ω1>ω2>ω3>0, and ω1+ω2+ω3=1; Among them, u(t) is the filling speed adjustment at the future time n+Δt, K p , K i , K d are the control parameters of proportion, integration and differentiation respectively; The filling pressure is compared with the preset filling pressure threshold range. If the filling pressure is within the preset filling pressure threshold range, D P =0, if the filling pressure is lower than the preset minimum filling pressure threshold, the formula for obtaining the filling pressure hazard index is: Among them, D P is the filling pressure hazard index, P(n) is the filling pressure at the current moment, P min is the minimum value of the filling pressure threshold range; If the filling pressure is greater than the preset maximum filling pressure threshold value, the formula for obtaining the filling pressure hazard index is: Among them, P max is the maximum value of the filling pressure threshold range; Similarly, obtain the filling speed hazard index D V , Filling temperature hazard index D T ; The formula for obtaining the filling risk index at the current moment is: D=λ1·D P +λ2·D V +λ3·D T Where D is the filling risk index at the current moment, λ1, λ2, and λ3 are risk weight indexes, λ1>λ2>λ3>0, and λ1+λ2+λ3=1; Among them, the threshold range of filling pressure is [0.2, 1.5], the unit is MPa, the threshold range of filling speed is [10, 100], the unit is L / min, the threshold unit of filling temperature is [5, 60], the unit is ℃. If the filling hazard index at the current moment is higher than the preset hazard threshold, the filling system will issue a hazard warning. If the filling hazard index at the current moment is lower than the preset hazard threshold, the filling system will continue to be monitored.

Images