Fuel gas safety magic cube and monitoring method thereof

Abstract

The embodiment of the invention discloses a fuel gas safety Rubik's cube and a monitoring method thereof. The Rubik's cube comprises an electromagnetic valve used for controlling opening / closing of a fuel gas pipeline, a pressure sensor used for detecting the pressure of the fuel gas pipeline and a temperature sensor used for detecting the environment temperature. When the fuel gas safety magic cube detects that the real-time pressure value P of the fuel gas pipeline exceeds the maximum pressure preset value PMAX or is smaller than the minimum pressure preset value PMIN through the pressure sensor, the electromagnetic valve is closed. According to the self-closing valve, abnormity detection is more accurate, and the abnormity can be directly improved to be within 5% compared with an existing self-closing valve 25%; the state can be updated in real time, and a user is clear at a glance; according to the invention, the security check of the gas company can be remote, and the operation cost is greatly reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of gas, and in particular to a gas safety magic cube and a monitoring method thereof. Background Art

[0002] Each device of the existing gas system works relatively independently, and there is no systematic and comprehensive monitoring of the safety risks of gas use. The existing gas system has the following problems: the existing detection methods for minute leaks are relatively complex and often require manual on-site detection; the alarm and the cut-off valve are installed in front of the gas meter, while the alarm is installed near the gas use area, which is prone to problems such as long distance, partition walls, etc., resulting in inconvenient wiring, unbeautiful wiring, and affecting the user's impression; the pipeline pressure cannot be intuitively detected, and customer complaints are likely to occur when there is abnormal gas use, resulting in an increase in the workload of customer service; there are many devices, and it is difficult to troubleshoot when the user cannot use gas; the status of each device cannot be remotely viewed at any time. Summary of the Invention

[0003] The technical problem to be solved by the embodiments of the present invention is to provide a gas safety magic cube and a monitoring method thereof to reduce the safety risks of gas use.

[0004] To solve the above technical problem, an embodiment of the present invention provides a gas safety magic cube, including a solenoid valve for controlling the opening / closing of a gas pipeline, a pressure sensor for detecting the pressure of the gas pipeline, and a temperature sensor for detecting the ambient temperature; wherein, when the gas safety magic cube detects through the pressure sensor that the real-time pressure value P of the gas pipeline exceeds the maximum pressure preset value P MAX or is less than the minimum pressure preset value P MIN the solenoid valve is closed.

[0005] Further, after the gas safety magic cube closes the solenoid valve, it maintains pressure for a first preset time t. If the pipeline pressure does not decrease by more than a preset range e% within the first preset time t, it is determined that there is a minute leak in the pipeline, and abnormal information is uploaded.

[0006] Further, when the gas safety magic cube satisfies P IN -P OUT >P ΔSET it is determined that there is an overcurrent abnormality in the gas pipeline. If the duration of the overcurrent abnormality exceeds the preset duration t PRD the solenoid valve is closed and overcurrent abnormal information is uploaded; wherein, P IN is the average value of the gas pipeline pressure within a second preset time before the user uses gas, P OUT is the maximum value of the gas pipeline pressure within a third preset time when the user uses gas normally, and P ΔSET is a preset pressure value.

[0007] Further, the initial PΔSET Obtained through laboratory testing and through long-term self-learning, P can be continuously adjusted and optimized ΔSET , so as to achieve dynamic convergence and optimal effect.

[0008] Furthermore, the gas safety cube adjusts and optimizes P according to the following steps ΔSET : (1) Data collection and preprocessing: Real-time recording of the average pressure P before the user uses gas IN_AV And the maximum pressure P when using gas OUT_MAX , calculate the pressure difference P in historical data Δ and its duration, ; (2) Initial value setting: Determine the initial P through testing ΔSET ; (3) Adaptive learning: Sliding window statistics: Use the data in the time window to calculate P Δ distribution characteristics of Threshold optimization: If the current P ΔSET If the valve is frequently shut down by mistake, the P ΔSET ; If the overcurrent anomaly is missed, reduce P ΔSET ; Gradient descent method: Minimize the false positive / false negative rate as the objective function, and adjust P by iterative ΔSET , gradually approaching the optimal solution; (4) Dynamic convergence: Stability judgment: When P ΔSET When the adjustment range is less than the preset threshold, it is determined to be converged; Abnormal restart mechanism: If the environment or user gas usage patterns change significantly, the system will re-enter the learning phase to avoid overfitting; (5) Personalized adjustments: Each gas safety cube independently stores user data and adjusts P based on local historical records. ΔSET , to avoid cross-user data interference.

[0009] Furthermore, the gas safety cube detects the ambient temperature T in real time when the temperature sensor detects the ambient temperature T ENV Exceeds the preset temperature value T SET Close the solenoid valve and upload the temperature abnormality information.

[0010] Furthermore, the gas safety cube also includes a communication module for pushing messages to a server platform.

[0011] Furthermore, the gas safety magic cube also includes an audio-visual device for alerting the user.

[0012] Accordingly, an embodiment of the present invention further provides a monitoring method for a gas safety magic cube, including: Step 1: Construct a multi-dimensional pressure fluctuation feature database, where the feature database includes a normal working condition database and an abnormal working condition data set; Step 2: Construct a feature recognition model using a deep neural network architecture, use the normal working condition database as the basic training set, and use the abnormal data set as the reinforcement training set to train the feature recognition model; Step 3: Optimize the feature recognition model using knowledge distillation; Step 4: Continuously monitor the pressure fluctuation features through the feature recognition model that has completed knowledge distillation, trigger a hierarchical early warning mechanism based on the monitoring results, store the abnormal event waveforms, and perform remote transmission, and dynamically adjust the detection sensitivity based on an adaptive threshold.

[0013] The beneficial effects of the present invention are as follows: The present invention is more accurate in detecting abnormalities, and can directly improve from 25% of the existing self-closing valve to within 5%; the state of the present invention can be updated in real time, which is clear at a glance for users; the present invention enables remote safety inspections by gas companies, greatly reducing operating costs. Specific Embodiments

[0014] It should be noted that, without conflict, the embodiments in the present application and the features in the embodiments can be combined with each other. The present invention will be further described in detail below with reference to specific embodiments.

[0015] The gas safety magic cube of the embodiment of the present invention is installed on a gas pipeline. The gas safety magic cube includes an electromagnetic valve for controlling the opening / closing of the gas pipeline, a pressure sensor for detecting the pressure of the gas pipeline, a temperature sensor for detecting the ambient temperature, a communication module for pushing messages to a server platform, a sound and light device for alarming users, etc.

[0016] For overpressure / underpressure valve closing in the present invention, by adding a pressure sensor to detect the pressure value P in real time and comparing it with the set pressure values (maximum pressure value P MAX , minimum pressure value P MIN ), the electromagnetic valve state V0 is output, and the formula is . The initial value of the maximum pressure value P MAX is 8 kPa, and the initial value of the minimum pressure value P MIN is 0.8 kPa.

[0017] For minor leaks in the present invention, after closing the electromagnetic valve, record the pressure value at this time as P, keep the pressure for a period of time, that is, the first preset time t (the initial value is 5 minutes). If the pipeline pressure does not drop by more than e% during this period, it can be determined that there is a minor leak in the pipeline, that is .

[0018] As an implementation method, the implementation principle of the overcurrent function of the present invention is as follows: (1) Assume that the pressure before the valve is P IN , and the pressure after the valve is P OUT . When is greater than the set value P ΔSET , it can be determined that there is an overcurrent abnormality in the pipeline. If the duration exceeds the preset duration t PRD , the device will immediately close the valve. The preset duration t PRD has an initial value of 5 seconds.

[0019] (2) P IN is a relatively static value, which is obtained from the average value P IN_AV of the gas pipeline pressure within a period of time before the user uses gas.

[0020] (3) P OUT is a real-time value, which is in a fluctuating state during normal gas use by the user. Record the data for a period of time, and then take the maximum value as a calculation parameter in (1) after cleaning the data set.

[0021] (4) For P ΔSET , it can be given through laboratory tests. The test method refers to the above (2) and (3). The initial value of P ΔSET is 100 Pa.

[0022] For the user scenarios with thousands of different faces, through long-term self-learning, P ΔSET can be continuously adjusted and optimized

[0023] The gas safety magic cube adjusts and optimizes P ΔSET according to the following steps: (1) Data collection and preprocessing: Real-time record the average pressure (P IN_AV ) before the user uses gas and the maximum pressure (P OUT_MAX ) during gas use. Statistically analyze the pressure difference ( ) and its duration in the historical data.

[0024] (2) Initial value setting: Simulate typical working conditions in the laboratory, and determine the initial P ΔSET through a large number of tests, covering common overcurrent abnormal scenarios.

[0025] (3) Adaptive learning algorithm: Sliding window statistics: Use the data within a time window (such as one week or one month) to calculate the distribution characteristics (mean value, variance) of P Δ .

[0026] Threshold optimization: If the current P ΔSETIf it leads to frequent false valve closures (false alarms), then appropriately increase P ΔSET ; If an overcurrent anomaly is missed (missed alarm), then decrease P ΔSET .

[0027] Gradient descent method: Using the minimization of false alarm / missed alarm rate as the objective function, iteratively adjust P ΔSET , and gradually approach the optimal solution.

[0028] (4) Dynamic convergence mechanism: Stability judgment: When the adjustment amplitude of P within multiple consecutive time windows is less than a preset threshold (such as ±1%), it is determined to converge. ΔSET

[0029] Abnormal restart mechanism: If there are significant changes in the environment or the user's gas usage pattern (such as seasonal changes), the system re - enters the learning stage to avoid overfitting.

[0030] (5) Personalized adjustment: Each gas safety cube independently stores user data and adjusts P according to local historical records ΔSET , avoiding cross - user data interference.

[0031] The implementation principle of the high - temperature valve closure in the present invention is: Real - time detection of the environmental temperature T ENV , and automatically close the valve when it exceeds the set value T SET , that is .

[0032] When any of the above - mentioned states changes, the present invention automatically sends a push message to the server platform to inform the user.

[0033] The present invention can detect and respond to gas leakage in a timely manner, ensuring user safety. While realizing function upgrades, the present invention reduces the user's purchase cost and the operation and maintenance investment of the gas company.

[0034] The monitoring method of the gas safety cube in the embodiment of the present invention includes steps 1 to 4.

[0035] Step 1: Construct a multi - dimensional pressure fluctuation feature database, and the feature database includes a normal working condition database and an abnormal working condition data set.

[0036] Under a controlled experimental environment, construct a multi - dimensional pressure fluctuation feature database: (1) Collect the pressure fluctuation data of the gas system under standard working conditions, establish a normal working condition database including multi - parameter coupling such as temperature, flow rate, and valve status, and the cumulative collection duration is not less than 1000 equipment hours.

[0037] (2) Construct an abnormal working condition data set, including: ​For the instantaneous large-flow leakage scenario caused by the complete detachment of the pipeline, the continuous leakage scenario caused by tiny holes (Φ≤2mm), and the composite leakage condition (leakage in the middle section of the pipeline and normal gas consumption at the end), pressure waveform data is collected by a precision pressure sensor (accuracy class 0.5) at a sampling frequency of not less than 100Hz.

[0038] Step 2: Construct a feature recognition model using a deep neural network architecture. Use the normal condition database as the basic training set and the abnormal data set as the enhanced training set to train the feature recognition model.

[0039] The present invention constructs a feature recognition model using a deep neural network architecture: (1) Perform wavelet denoising preprocessing on the original pressure data to extract the time-frequency domain joint feature vector; (2) Through the transfer learning strategy, use the normal condition database as the basic training set and the abnormal data set as the enhanced training set; (3) Design a multi-task learning network to synchronously optimize the three sub-tasks of leakage detection, leakage level assessment, and leakage type classification.

[0040] Step 3: Optimize the feature recognition model using knowledge distillation. (1) The present invention uses knowledge distillation technology to compress the complex model into a lightweight model suitable for edge computing, reducing the computational amount by 85% while maintaining a detection accuracy of more than 98%; (2) Develop an embedded inference engine to achieve real-time response at the Ms level and control the memory occupancy within 512KB.

[0041] Step 4: Continuously monitor the pressure fluctuation characteristics through the feature recognition model that has completed knowledge distillation, trigger a hierarchical early warning mechanism based on the monitoring results, store the abnormal event waveforms, and perform remote transmission, as well as dynamically adjust the detection sensitivity based on an adaptive threshold.

[0042] During specific implementation, a pressure sensing module, an edge computing unit, and an early warning module can be integrated into the terminal device to construct a complete system with the following functions: continuously monitoring the pressure fluctuation characteristics, dynamically adjusting the detection sensitivity based on an adaptive threshold, hierarchical early warning mechanism (early warning / alarm / emergency shutdown), storage and remote transmission of abnormal event waveforms, etc.

[0043] Although the embodiments of the present invention have been shown and described, for those of ordinary skill in the art, it can be understood that various changes, modifications, substitutions, and variations can be made to these embodiments without departing from the principle and spirit of the present invention. The scope of the present invention is defined by the appended claims and their equivalent scope.

Claims

1. A gas safety magic cube, characterized in that, The gas safety cube includes a solenoid valve for controlling the opening / closing of a gas pipeline, a pressure sensor for detecting the pressure of the gas pipeline, and a temperature sensor for detecting the ambient temperature; wherein, when the gas safety cube detects that the real-time pressure value P of the gas pipeline exceeds the maximum pressure preset value P MAX or is less than the minimum pressure preset value P MIN , the solenoid valve is closed.

2. The gas safety magic cube according to claim 1, wherein After the gas safety magic cube closes the solenoid valve, it maintains pressure for the first preset time t. If the pipeline pressure does not drop by more than the preset range e% within the first preset time t, it is determined that there is a micro-leakage in the pipeline, and abnormal information is uploaded.

3. The gas safety magic cube according to claim 1, characterized in that When the gas safety magic cube satisfies P IN -P OUT >P ΔSET it is determined that there is an overcurrent anomaly in the gas pipeline. If the duration of the overcurrent anomaly exceeds the preset duration t PRD the solenoid valve is closed and the overcurrent anomaly information is uploaded; where P IN is the average value of the gas pipeline pressure within the second preset time before the user uses gas, P OUT is the maximum value of the gas pipeline pressure within the third preset time during normal gas use by the user, and P ΔSET is the preset pressure value.

4. The gas safety magic cube according to claim 3, characterized in that, Initial P ΔSET Obtained through laboratory tests and continuously adjusted and optimized through long-term self-learning ΔSET to achieve dynamic convergence and optimal results.

5. The gas safety magic cube according to claim 4, characterized in that, The gas safety magic cube adjusts and optimizes P according to the following steps ΔSET : (1) Data collection and preprocessing: Real-time record the average pressure P before the user uses gas IN_AV and the maximum pressure P during gas use OUT_MAX , and statistically analyze the pressure difference P Δ in the historical data and its duration ; (2)Initial value setting: Determine the initial P through testing ΔSET ; (3) Adaptive learning: Sliding window statistics: Using the data within the time window, calculate the distribution characteristics of P Δ ; Threshold optimization: If the current P ΔSET causes frequent false valve closures, then appropriately increase P ΔSET ; If the overcurrent anomaly is missed, then reduce P ΔSET ; Gradient descent method: Taking the minimization of false alarm / miss rate as the objective function, iteratively adjust P ΔSET , and gradually approach the optimal solution; (4) Dynamic convergence: Stability judgment: When the adjustment amplitude of P ΔSET is less than the preset threshold within multiple consecutive time windows, it is determined to converge; Abnormal restart mechanism: If the environment or the user's gas usage pattern changes significantly, it will re-enter the learning stage to avoid overfitting; (5) Personalized adjustment: Each gas safety magic cube stores user data independently and adjusts P according to local historical records ΔSET , avoiding cross-user data interference.

6. The gas safety magic cube according to claim 1, characterized in that, When the ambient temperature T is detected in real time by the temperature sensor of the gas safety magic cube ENV exceeds the preset temperature value T SET the solenoid valve is closed and the temperature anomaly information is uploaded.

7. The gas safety magic cube according to claim 1, characterized in that, The gas safety magic cube further includes a communication module for pushing messages to the server platform.

8. The gas safety magic cube according to claim 1, wherein The gas safety magic cube further includes an audible and visual device for alarming the user.

9. A monitoring method for a gas safety magic cube as described in any one of claims 1-8, characterized in that, Include: Step 1: Construct a multi-dimensional pressure fluctuation feature database, which includes a normal working condition database and an abnormal working condition data set; Step 2: Construct a feature recognition model using a deep neural network architecture, use the normal working condition database as the basic training set, and use the abnormal data set as the enhanced training set to train the feature recognition model; Step 3: Optimize the feature recognition model using knowledge distillation; Step 4: Continuously monitor the pressure fluctuation characteristics through the feature recognition model that has completed knowledge distillation, trigger a hierarchical early warning mechanism based on the monitoring results, store the abnormal event waveform, and perform remote transmission, and dynamically adjust the detection sensitivity based on an adaptive threshold.